Port CLGEMM to memory injecting interface

Moves the following kernels:
 - CLGEMMMatrixMultiplyKernel
 - CLGEMMMatrixMultiplyNativeKernel
 - CLGEMMMatrixMultipluReshapedKernel
 - CLGEMMMatrixMultiplyReshapedOnlyRHSKernel

 Moves the following functions
 - CLGEMM

Introduces facilities to easy handling of auxiliary temporary buffers
under then new run interface. Such are:
 - CLAuxTensorHandler: That allows wrapping of workspace buffers memory
 to CLBuffer objects
 - Ability to inject TensorInfo to allocator without transferring
 ownership. This reduce the copy overhead if needed.

Resolves: COMPMID-4188

Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I7055435d831b05b749b26302082e4ac45f26dfb0
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5498
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

105 files changed, 4473 insertions, 3202 deletions

diff --git a/Android.bp b/Android.bp
index d04cecc74e..4be2bfd55d 100644
--- a/Android.bp
+++ b/Android.bp
@@ -75,14 +75,6 @@ cc_library_static {
         "src/core/CL/ICLSimpleKernel.cpp",
         "src/core/CL/ICLTensor.cpp",
         "src/core/CL/OpenCL.cpp",
-        "src/core/CL/gemm/CLGEMMHelpers.cpp",
-        "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.cpp",
-        "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.cpp",
-        "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.cpp",
-        "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.cpp",
-        "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.cpp",
-        "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp",
-        "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp",
         "src/core/CL/kernels/CLArgMinMaxLayerKernel.cpp",
         "src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp",
         "src/core/CL/kernels/CLBatchToSpaceLayerKernel.cpp",
@@ -112,12 +104,6 @@ cc_library_static {
         "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.cpp",
         "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleKernel.cpp",
         "src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp",
-        "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp",
-        "src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp",
-        "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp",
-        "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp",
-        "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.cpp",
-        "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.cpp",
         "src/core/CL/kernels/CLGatherKernel.cpp",
         "src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp",
         "src/core/CL/kernels/CLIm2ColKernel.cpp",
@@ -367,6 +353,12 @@ cc_library_static {
         "src/core/gpu/cl/kernels/ClElementwiseUnaryKernel.cpp",
         "src/core/gpu/cl/kernels/ClFillKernel.cpp",
         "src/core/gpu/cl/kernels/ClFloorKernel.cpp",
+        "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp",
+        "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp",
+        "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.cpp",
+        "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp",
+        "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.cpp",
+        "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.cpp",
         "src/core/gpu/cl/kernels/ClHeightConcatenateKernel.cpp",
         "src/core/gpu/cl/kernels/ClMulKernel.cpp",
         "src/core/gpu/cl/kernels/ClPermuteKernel.cpp",
@@ -379,6 +371,16 @@ cc_library_static {
         "src/core/gpu/cl/kernels/ClWidthConcatenate2TensorsKernel.cpp",
         "src/core/gpu/cl/kernels/ClWidthConcatenate4TensorsKernel.cpp",
         "src/core/gpu/cl/kernels/ClWidthConcatenateKernel.cpp",
+        "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.cpp",
+        "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp",
+        "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.cpp",
+        "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.cpp",
+        "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.cpp",
+        "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.cpp",
+        "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.cpp",
+        "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.cpp",
+        "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp",
+        "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp",
         "src/core/helpers/SoftmaxHelpers.cpp",
         "src/core/helpers/WindowHelpers.cpp",
         "src/core/utils/ScaleUtils.cpp",
@@ -664,6 +666,7 @@ cc_library_static {
         "src/runtime/gpu/cl/operators/ClFill.cpp",
         "src/runtime/gpu/cl/operators/ClFlatten.cpp",
         "src/runtime/gpu/cl/operators/ClFloor.cpp",
+        "src/runtime/gpu/cl/operators/ClGemm.cpp",
         "src/runtime/gpu/cl/operators/ClLogicalNot.cpp",
         "src/runtime/gpu/cl/operators/ClMul.cpp",
         "src/runtime/gpu/cl/operators/ClPRelu.cpp",
diff --git a/SConscript b/SConscript
index 22054cb61d..a009d1f007 100644
--- a/SConscript
+++ b/SConscript
@@ -220,12 +220,18 @@ if env['openmp']:
      runtime_files += Glob('src/runtime/OMP/OMPScheduler.cpp')
 
 if env['opencl']:
+    cl_kernel_hp_files = ['src/core/gpu/cl/kernels/gemm/ClGemmHelpers.cpp',
+                          'src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp',
+                          'src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.cpp',
+                          'src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.cpp',
+                          'src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.cpp',
+                          'src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.cpp',
+                          'src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.cpp',
+                          'src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.cpp',
+                          ]
+    core_files += cl_kernel_hp_files
     core_files += Glob('src/core/CL/*.cpp')
     core_files += Glob('src/core/CL/kernels/*.cpp')
-    core_files += Glob('src/core/CL/gemm/*.cpp')
-    core_files += Glob('src/core/CL/gemm/native/*.cpp')
-    core_files += Glob('src/core/CL/gemm/reshaped/*.cpp')
-    core_files += Glob('src/core/CL/gemm/reshaped_only_rhs/*.cpp')
     core_files += Glob('src/core/gpu/cl/*.cpp')
     core_files += Glob('src/core/gpu/cl/kernels/*.cpp')
 
diff --git a/arm_compute/core/ITensorPack.h b/arm_compute/core/ITensorPack.h
index 8aea880bb6..2f41d4d51e 100644
--- a/arm_compute/core/ITensorPack.h
+++ b/arm_compute/core/ITensorPack.h
@@ -24,9 +24,11 @@
 #ifndef ARM_COMPUTE_ITENSORPACK_H
 #define ARM_COMPUTE_ITENSORPACK_H
 
+#include "arm_compute/core/experimental/Types.h"
+
 #include <cstddef>
 #include <cstdint>
-#include <map>
+#include <unordered_map>
 
 namespace arm_compute
 {
@@ -36,19 +38,20 @@ class ITensor;
 /** Tensor packing service */
 class ITensorPack
 {
-private:
+public:
     struct PackElement
     {
         PackElement() = default;
-        PackElement(ITensor *tensor)
-            : tensor(tensor), ctensor(nullptr)
+        PackElement(int id, ITensor *tensor)
+            : id(id), tensor(tensor), ctensor(nullptr)
         {
         }
-        PackElement(const ITensor *ctensor)
-            : tensor(nullptr), ctensor(ctensor)
+        PackElement(int id, const ITensor *ctensor)
+            : id(id), tensor(nullptr), ctensor(ctensor)
         {
         }
 
+        int            id{ -1 };
         ITensor       *tensor{ nullptr };
         const ITensor *ctensor{ nullptr };
     };
@@ -56,6 +59,8 @@ private:
 public:
     /** Default Constructor */
     ITensorPack() = default;
+    /**  Initializer list Constructor */
+    ITensorPack(std::initializer_list<PackElement> l);
     /** Add tensor to the pack
      *
      * @param[in] id     ID/type of the tensor to add
@@ -102,7 +107,7 @@ public:
     bool empty() const;
 
 private:
-    std::map<unsigned int, PackElement> _pack{}; /**< Container with the packed tensors */
+    std::unordered_map<int, PackElement> _pack{}; /**< Container with the packed tensors */
 };
 } // namespace arm_compute
 #endif /*ARM_COMPUTE_ITENSORPACK_H */
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index 9e054f26dd..ec9c419dbc 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -1753,11 +1753,11 @@ private:
 
 /** GEMM reshape information class. This class stores the necessary information about matrix A and matrix B reshape.
  *
- * The matrix A can only be reshaped through @ref CLGEMMReshapeLHSMatrixKernel or  @ref NEGEMMInterleave4x4Kernel
- * Note: Optionally just for @ref CLGEMMReshapeLHSMatrixKernel is it possible to set mult_interleave4x4_height, the multiplication factor for the height of the 4x4 interleaved block
+ * The matrix A can only be reshaped through @ref opencl::kernels::ClGemmReshapeLhsMatrixKernel or  @ref NEGEMMInterleave4x4Kernel
+ * Note: Optionally just for @ref opencl::kernels::ClGemmReshapeLhsMatrixKernel is it possible to set mult_interleave4x4_height, the multiplication factor for the height of the 4x4 interleaved block
  *
- * The matrix B can only be reshaped through @ref CLGEMMReshapeRHSMatrixKernel or  @ref NEGEMMTranspose1xWKernel
- * Note: Optionally just for @ref CLGEMMReshapeRHSMatrixKernel is it possible to set mult_transpose1xW_width, the multiplication factor for the width of the 1xW transposed block
+ * The matrix B can only be reshaped through @ref opencl::kernels::ClGemmReshapeRhsMatrixKernel or  @ref NEGEMMTranspose1xWKernel
+ * Note: Optionally just for @ref opencl::kernels::ClGemmReshapeRhsMatrixKernel is it possible to set mult_transpose1xW_width, the multiplication factor for the width of the 1xW transposed block
  *
  */
 class GEMMReshapeInfo final
diff --git a/arm_compute/core/experimental/Types.h b/arm_compute/core/experimental/Types.h
index 7ddb930421..92ece460dc 100644
--- a/arm_compute/core/experimental/Types.h
+++ b/arm_compute/core/experimental/Types.h
@@ -47,6 +47,7 @@ enum TensorType : int32_t
     ACL_DST_0   = 30,
     ACL_DST_1   = 31,
     ACL_DST_2   = 32,
+    ACL_BIAS    = ACL_SRC_2,
     ACL_INT     = 50,
     ACL_INT_0   = 50,
     ACL_INT_1   = 51,
@@ -54,21 +55,40 @@ enum TensorType : int32_t
     ACL_INT_3   = 53,
     ACL_INT_4   = 54,
     ACL_SRC_VEC = 256,
+    ACL_DST_VEC = 512,
+    ACL_INT_VEC = 1024
 };
 
 namespace experimental
 {
+enum class MemoryLifetime
+{
+    Temporary  = 0,
+    Persistent = 1,
+    Prepare    = 2,
+};
 struct MemoryInfo
 {
-    MemoryInfo(TensorType type, size_t size, size_t alignment) noexcept
-        : type(type),
+    MemoryInfo() = default;
+
+    MemoryInfo(int slot, size_t size, size_t alignment = 0) noexcept
+        : slot(slot),
+          size(size),
+          alignment(alignment)
+    {
+    }
+
+    MemoryInfo(int slot, MemoryLifetime lifetime, size_t size, size_t alignment = 0) noexcept
+        : slot(slot),
+          lifetime(lifetime),
           size(size),
           alignment(alignment)
     {
     }
-    TensorType type;
-    size_t     size;
-    size_t     alignment;
+    int            slot{ ACL_UNKNOWN };
+    MemoryLifetime lifetime{ MemoryLifetime::Temporary };
+    size_t         size{ 0 };
+    size_t         alignment{ 64 };
 };
 
 using MemoryRequirements = std::vector<MemoryInfo>;
diff --git a/arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h b/arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h
index eec01bcebe..075c5d1f45 100644
--- a/arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h
+++ b/arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h
@@ -98,7 +98,7 @@ private:
  *
  *  -# @ref CLIm2ColKernel (called when the input comes from a convolutional layer)
  *  -# @ref CLTranspose (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
- *  -# @ref CLGEMMMatrixMultiplyKernel or @ref CLGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
+ *  -# @ref opencl::kernels::ClGemmMatrixMultiplyKernel or @ref CLGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
  *
  * @note  The fully connected layer accepts "weights" tensors only with 2 dimensions.
  */
diff --git a/arm_compute/runtime/CL/functions/CLGEMM.h b/arm_compute/runtime/CL/functions/CLGEMM.h
index 1e2ae7be64..38a07ef9fb 100644
--- a/arm_compute/runtime/CL/functions/CLGEMM.h
+++ b/arm_compute/runtime/CL/functions/CLGEMM.h
@@ -35,76 +35,12 @@
 
 namespace arm_compute
 {
+// Forward declarations
 class CLCompileContext;
-class CLGEMMReshapeRHSMatrixKernel;
-class CLGEMMMatrixMultiplyKernel;
-class CLGEMMMatrixMultiplyReshapedKernel;
-class CLGEMMMatrixMultiplyReshapedOnlyRHSKernel;
-class CLGEMMReshapeLHSMatrixKernel;
 class ICLTensor;
 class ITensorInfo;
 
-namespace weights_transformations
-{
-/** Basic function to manage the reshape weights generated from @ref CLGEMMReshapeRHSMatrixKernel */
-class CLGEMMReshapeRHSMatrixKernelManaged : public ITransformWeights
-{
-public:
-    /** Default constructor */
-    CLGEMMReshapeRHSMatrixKernelManaged();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMReshapeRHSMatrixKernelManaged(const CLGEMMReshapeRHSMatrixKernelManaged &) = delete;
-    /** Default move constructor */
-    CLGEMMReshapeRHSMatrixKernelManaged(CLGEMMReshapeRHSMatrixKernelManaged &&) = default;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMReshapeRHSMatrixKernelManaged &operator=(const CLGEMMReshapeRHSMatrixKernelManaged &) = delete;
-    /** Default move assignment operator */
-    CLGEMMReshapeRHSMatrixKernelManaged &operator=(CLGEMMReshapeRHSMatrixKernelManaged &&) = default;
-    /** Default desctructor */
-    ~CLGEMMReshapeRHSMatrixKernelManaged();
-    //Inherited method override
-    void run() override;
-
-    //Inherited method override
-    void release() override;
-
-    //Inherited method override
-    ICLTensor *get_weights() override;
-
-    //Inherited method override
-    uint32_t uid() override;
-
-    /** Configures the @ref CLGEMMReshapeRHSMatrixKernel kernel
-     *
-     * @param[in] input Input tensor. Data types supported: All
-     * @param[in] info  RHS matrix information to be used for reshaping.
-     */
-    void configure(const ICLTensor *input, GEMMRHSMatrixInfo info);
-
-    /** Configures the @ref CLGEMMReshapeRHSMatrixKernel kernel
-     *
-     * @param[in] compile_context The compile context to be used.
-     * @param[in] input           Input tensor. Data types supported: All
-     * @param[in] info            RHS matrix information to be used for reshaping.
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input, GEMMRHSMatrixInfo info);
-
-private:
-    static constexpr uint32_t                     _uid{ 0x15 };
-    CLTensor                                      _output{};
-    std::unique_ptr<CLGEMMReshapeRHSMatrixKernel> _kernel;
-};
-} // namespace weights_transformations
-
-/** Basic function to execute GEMM on OpenCL. This function calls the following OpenCL kernels:
- *
- *  -# @ref CLGEMMReshapeLHSMatrixKernel (only if the RESHAPED_V1 is selected by the heuristic model)
- *  -# @ref CLGEMMReshapeRHSMatrixKernel (only if either the RESHAPED_V1 or RESHAPED_ONLY_RHS is selected by the select_gemm_kernel method())
- *  -# @ref CLGEMMMatrixMultiplyKernel (only if either the NATIVE or RESHAPED_V1 is selected by the select_gemm_kernel method())
- *  -# @ref CLGEMMMatrixMultiplyReshapedKernel (only if RESHAPED_V1 is selected by the select_gemm_kernel method())
- *  -# @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel (only if RESHAPED_ONLY_RHS is selected by the select_gemm_kernel method())
- *
- */
+/** Basic function to execute GEMM on OpenCL */
 class CLGEMM : public IFunction
 {
 public:
@@ -114,16 +50,16 @@ public:
      * @param[in] weights_manager (Optional) Weights manager.
      */
     CLGEMM(std::shared_ptr<IMemoryManager> memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
+    /** Default destructor */
+    ~CLGEMM();
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     CLGEMM(const CLGEMM &) = delete;
     /** Default move constructor */
-    CLGEMM(CLGEMM &&) = default;
+    CLGEMM(CLGEMM &&);
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     CLGEMM &operator=(const CLGEMM &) = delete;
     /** Default move assignment operator */
-    CLGEMM &operator=(CLGEMM &&) = default;
-    /** Default destructor */
-    ~CLGEMM();
+    CLGEMM &operator=(CLGEMM &&);
     /** Initialise the kernel's inputs and output
      *
      * Valid data layouts:
@@ -141,25 +77,6 @@ public:
      *
      * @note Whilst the first input tensor can be a vector, the second input tensor must be at least a matrix
      *
-     * @param[in]  a         First input tensor  (Matrix or Vector A). Data types supported: F16/F32
-     * @param[in]  b         Second input tensor (Matrix B). Data type supported: same as @p a.
-     * @param[in]  c         Third input tensor  (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a.
-     * @param[out] output    Output tensor. Data type supported: same as @p a
-     * @param[in]  alpha     Weight of the matrix product
-     * @param[in]  beta      Weight of matrix C
-     * @param[in]  gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
-     *                       if the reshape of matrix B should happen only for the first run. GEMMInfo also contains information about the reshaping
-     *                       in case matrix A and matrix B have been already transformed.
-     */
-    void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info = GEMMInfo());
-    /** Initialise the kernel's inputs and output
-     *
-     * @note GEMM: General Matrix Multiply - [alpha * A * B + beta * C].
-     *
-     * @note All tensors must have the same data type.
-     *
-     * @note Whilst the first input tensor can be a vector, the second input tensor must be at least a matrix
-     *
      * @param[in]  compile_context The compile context to be used.
      * @param[in]  a               First input tensor  (Matrix or Vector A). Data types supported: F16/F32
      * @param[in]  b               Second input tensor (Matrix B). Data type supported: same as @p a.
@@ -168,20 +85,20 @@ public:
      * @param[in]  alpha           Weight of the matrix product
      * @param[in]  beta            Weight of matrix C
      * @param[in]  gemm_info       (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
-     *                       if the reshape of matrix B should happen only for the first run. GEMMInfo also contains information about the reshaping
-     *                       in case matrix A and matrix B have been already transformed.
+     *                             if the reshape of matrix B should happen only for the first run. GEMMInfo also contains information about the reshaping
+     *                             in case matrix A and matrix B have been already transformed.
      */
     void configure(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info = GEMMInfo());
+
+    /** Initialise the kernel's inputs and output
+     *
+     * Similar to @ref CLGEMM::configure()
+     */
+    void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info = GEMMInfo());
+
     /** Static function to check if given info will lead to a valid configuration of @ref CLGEMM.
      *
-     * @param[in] a         First input tensor info  (Matrix or Vector A). Data types supported: F16/F32
-     * @param[in] b         Second input tensor info (Matrix B). Data type supported: same as @p a.
-     * @param[in] c         Third input tensor info  (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a.
-     * @param[in] output    Output tensor info. Data type supported: same as @p a
-     * @param[in] alpha     Weight of the matrix product
-     * @param[in] beta      Weight of matrix C
-     * @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
-     *                       if the reshape of matrix B should happen only for the first run
+     * Similar to @ref CLGEMM::configure()
      *
      * @return a status
      */
@@ -192,34 +109,8 @@ public:
     void prepare() override;
 
 private:
-    void configure_native_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info);
-    void configure_reshaped_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info);
-    void configure_reshaped_v2(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info);
-    void configure_reshaped_only_rhs(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                     const GEMMInfo &gemm_info);
-
-    static Status validate_native_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
-    static Status validate_reshaped_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
-    static Status validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
-    static Status validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
-
-    MemoryGroup                                                                   _memory_group;
-    IWeightsManager                                                              *_weights_manager;
-    std::unique_ptr<CLGEMMMatrixMultiplyKernel>                                   _mm_kernel;
-    std::unique_ptr<CLGEMMReshapeLHSMatrixKernel>                                 _reshape_lhs_kernel;
-    std::unique_ptr<CLGEMMReshapeRHSMatrixKernel>                                 _reshape_rhs_kernel;
-    std::unique_ptr<weights_transformations::CLGEMMReshapeRHSMatrixKernelManaged> _reshape_rhs_kernel_managed;
-    std::unique_ptr<CLGEMMMatrixMultiplyReshapedKernel>                           _mm_reshaped_kernel;
-    std::unique_ptr<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>                    _mm_reshaped_only_rhs_kernel;
-    std::unique_ptr<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>                    _mm_reshaped_only_rhs_fallback_kernel;
-    CLTensor                                                                      _tmp_a;
-    CLTensor                                                                      _tmp_b;
-    const ICLTensor                                                              *_original_b;
-    const ICLTensor                                                              *_lhs;
-    ICLTensor                                                                    *_dst;
-    bool                                                                          _reshape_b_only_on_first_run;
-    bool                                                                          _is_prepared;
-    CLGEMMKernelType                                                              _gemm_kernel_type;
+    struct Impl;
+    std::unique_ptr<Impl> _impl;
 };
 } // namespace arm_compute
 
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
index e7f4cb9d01..e5de45c34f 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
@@ -41,7 +41,13 @@ class CLGEMMLowpOffsetContributionKernel;
 class CLGEMMLowpOffsetContributionOutputStageKernel;
 class CLGEMMLowpMatrixAReductionKernel;
 class CLGEMMLowpMatrixBReductionKernel;
-class CLGEMMReshapeRHSMatrixKernel;
+namespace opencl
+{
+namespace kernels
+{
+class ClGemmReshapeRhsMatrixKernel;
+} // namespace kernels
+} // namespace opencl
 
 /** Basic function to execute GEMMLowpMatrixMultiplyCore on OpenCL. */
 class CLGEMMLowpMatrixMultiplyCore : public IFunction
@@ -140,7 +146,7 @@ private:
     std::unique_ptr<CLDepthConvertLayerKernel>                     _weights_to_qasymm8;
     std::unique_ptr<CLGEMMLowpMatrixMultiplyNativeKernel>          _mm_native_kernel;
     std::unique_ptr<CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel> _mm_reshaped_only_rhs_kernel;
-    std::unique_ptr<CLGEMMReshapeRHSMatrixKernel>                  _mtx_b_reshape_kernel;
+    std::unique_ptr<opencl::kernels::ClGemmReshapeRhsMatrixKernel> _mtx_b_reshape_kernel;
     std::unique_ptr<CLGEMMLowpMatrixAReductionKernel>              _mtx_a_reduction_kernel;
     std::unique_ptr<CLGEMMLowpMatrixBReductionKernel>              _mtx_b_reduction_kernel;
     std::unique_ptr<CLGEMMLowpOffsetContributionKernel>            _offset_contribution_kernel;
diff --git a/arm_compute/runtime/CL/functions/CLLogicalAnd.h b/arm_compute/runtime/CL/functions/CLLogicalAnd.h
index 61a15816eb..e3061e1dc3 100644
--- a/arm_compute/runtime/CL/functions/CLLogicalAnd.h
+++ b/arm_compute/runtime/CL/functions/CLLogicalAnd.h
@@ -34,37 +34,6 @@ class CLCompileContext;
 class ICLTensor;
 class ITensorInfo;
 
-namespace experimental
-{
-class CLLogicalAnd : public ICLOperator
-{
-public:
-    /** Default Constructor */
-    CLLogicalAnd() = default;
-    /** Initialise the kernel's inputs, output and conversion policy.
-     *
-     * @param[in]      compile_context The compile context to be used.
-     * @param[in, out] input1          First tensor input. Data types supported: U8.
-     *                                 The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
-     * @param[in, out] input2          Second tensor input. Data types supported: same as @p input1.
-     *                                 The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
-     * @param[out]     output          Output tensor. Data types supported: same as @p input1.
-     */
-    void configure(const CLCompileContext &compile_context, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output);
-    /** Static function to check if given info will lead to a valid configuration of @ref arm_compute::opencl::kernels::ClLogicalBinaryKernel
-     *
-     * @param[in] input1 First tensor input info. Data types supported: U8.
-     * @param[in] input2 Second tensor input info. Data types supported: same as @p input1.
-     * @param[in] output Output tensor info. Data types supported: same as @p input1.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
-    // Inherited methods overridden:
-    void run(ITensorPack &tensors) override;
-};
-} // namespace experimental
-
 /** Basic function to run @ref arm_compute::opencl::kernels::ClLogicalBinaryKernel.
  *
  * @note The tensor data type for the inputs must be U8.
@@ -125,5 +94,36 @@ private:
     struct Impl;
     std::unique_ptr<Impl> _impl;
 };
+
+namespace experimental
+{
+class CLLogicalAnd : public ICLOperator
+{
+public:
+    /** Default Constructor */
+    CLLogicalAnd() = default;
+    /** Initialise the kernel's inputs, output and conversion policy.
+     *
+     * @param[in]      compile_context The compile context to be used.
+     * @param[in, out] input1          First tensor input. Data types supported: U8.
+     *                                 The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[in, out] input2          Second tensor input. Data types supported: same as @p input1.
+     *                                 The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[out]     output          Output tensor. Data types supported: same as @p input1.
+     */
+    void configure(const CLCompileContext &compile_context, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref arm_compute::opencl::kernels::ClLogicalBinaryKernel
+     *
+     * @param[in] input1 First tensor input info. Data types supported: U8.
+     * @param[in] input2 Second tensor input info. Data types supported: same as @p input1.
+     * @param[in] output Output tensor info. Data types supported: same as @p input1.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
+    // Inherited methods overridden:
+    void run(ITensorPack &tensors) override;
+};
+} // namespace experimental
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_CLLOGICALAND_H */
diff --git a/arm_compute/runtime/CL/functions/CLLogicalOr.h b/arm_compute/runtime/CL/functions/CLLogicalOr.h
index b9ffb4a449..893c22f721 100644
--- a/arm_compute/runtime/CL/functions/CLLogicalOr.h
+++ b/arm_compute/runtime/CL/functions/CLLogicalOr.h
@@ -34,37 +34,6 @@ class CLCompileContext;
 class ICLTensor;
 class ITensorInfo;
 
-namespace experimental
-{
-class CLLogicalOr : public ICLOperator
-{
-public:
-    /** Default Constructor */
-    CLLogicalOr() = default;
-    /** Initialise the kernel's inputs, output and conversion policy.
-     *
-     * @param[in]      compile_context The compile context to be used.
-     * @param[in, out] input1          First tensor input. Data types supported: U8.
-     *                                 The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
-     * @param[in, out] input2          Second tensor input. Data types supported: same as @p input1.
-     *                                 The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
-     * @param[out]     output          Output tensor. Data types supported: same as @p input1.
-     */
-    void configure(const CLCompileContext &compile_context, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output);
-    /** Static function to check if given info will lead to a valid configuration of @ref arm_compute::opencl::kernels::ClLogicalBinaryKernel
-     *
-     * @param[in] input1 First tensor input info. Data types supported: U8.
-     * @param[in] input2 Second tensor input info. Data types supported: same as @p input1.
-     * @param[in] output Output tensor info. Data types supported: same as @p input1.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
-    // Inherited methods overridden:
-    void run(ITensorPack &tensors) override;
-};
-} // namespace experimental
-
 /** Basic function to run @ref arm_compute::opencl::kernels::ClLogicalBinaryKernel.
  *
  * @note The tensor data type for the inputs must be U8.
@@ -125,5 +94,36 @@ private:
     struct Impl;
     std::unique_ptr<Impl> _impl;
 };
+
+namespace experimental
+{
+class CLLogicalOr : public ICLOperator
+{
+public:
+    /** Default Constructor */
+    CLLogicalOr() = default;
+    /** Initialise the kernel's inputs, output and conversion policy.
+     *
+     * @param[in]      compile_context The compile context to be used.
+     * @param[in, out] input1          First tensor input. Data types supported: U8.
+     *                                 The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[in, out] input2          Second tensor input. Data types supported: same as @p input1.
+     *                                 The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[out]     output          Output tensor. Data types supported: same as @p input1.
+     */
+    void configure(const CLCompileContext &compile_context, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref arm_compute::opencl::kernels::ClLogicalBinaryKernel
+     *
+     * @param[in] input1 First tensor input info. Data types supported: U8.
+     * @param[in] input2 Second tensor input info. Data types supported: same as @p input1.
+     * @param[in] output Output tensor info. Data types supported: same as @p input1.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
+    // Inherited methods overridden:
+    void run(ITensorPack &tensors) override;
+};
+} // namespace experimental
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_CLLOGICALOR_H */
diff --git a/arm_compute/runtime/CL/functions/CLSlice.h b/arm_compute/runtime/CL/functions/CLSlice.h
index 7a7689c528..297bcd86fe 100644
--- a/arm_compute/runtime/CL/functions/CLSlice.h
+++ b/arm_compute/runtime/CL/functions/CLSlice.h
@@ -34,45 +34,6 @@ class ICLTensor;
 class CLCompileContext;
 class ITensorInfo;
 
-namespace experimental
-{
-/** Basic function to perform tensor slicing */
-class CLSlice : public ICLOperator
-{
-public:
-    /** Configure kernel
-     *
-     * @note Supported tensor rank: up to 4
-     * @note Start indices must be non-negative. 0 <= starts[i]
-     * @note End coordinates can be negative, which represents the number of elements before the end of that dimension.
-     * @note End indices are not inclusive unless negative.
-     *
-     * @param[in]  compile_context The compile context to be used.
-     * @param[in]  input           Source tensor info. Data type supported: All.
-     * @param[out] output          Destination tensor info. Data type supported: Same as @p input
-     * @param[in]  starts          The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
-     * @param[in]  ends            The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
-     */
-    void configure(const CLCompileContext &compile_context, const ITensorInfo *input, ITensorInfo *output, const Coordinates &starts, const Coordinates &ends);
-
-    /** Static function to check if given info will lead to a valid configuration of @ref CLSlice
-     *
-     * @note Supported tensor rank: up to 4
-     * @note Start indices must be non-negative. 0 <= starts[i]
-     * @note End coordinates can be negative, which represents the number of elements before the end of that dimension.
-     * @note End indices are not inclusive unless negative.
-     *
-     * @param[in] input  Source tensor info. Data type supported: All
-     * @param[in] output Destination tensor info. Data type supported: Same as @p input
-     * @param[in] starts The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
-     * @param[in] ends   The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
-     *
-     * @return A status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Coordinates &starts, const Coordinates &ends);
-};
-} // namespace experimental
-
 /** Basic function to perform tensor slicing */
 class CLSlice : public IFunction
 {
@@ -148,5 +109,44 @@ private:
     struct Impl;
     std::unique_ptr<Impl> _impl;
 };
+
+namespace experimental
+{
+/** Basic function to perform tensor slicing */
+class CLSlice : public ICLOperator
+{
+public:
+    /** Configure kernel
+     *
+     * @note Supported tensor rank: up to 4
+     * @note Start indices must be non-negative. 0 <= starts[i]
+     * @note End coordinates can be negative, which represents the number of elements before the end of that dimension.
+     * @note End indices are not inclusive unless negative.
+     *
+     * @param[in]  compile_context The compile context to be used.
+     * @param[in]  input           Source tensor info. Data type supported: All.
+     * @param[out] output          Destination tensor info. Data type supported: Same as @p input
+     * @param[in]  starts          The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
+     * @param[in]  ends            The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
+     */
+    void configure(const CLCompileContext &compile_context, const ITensorInfo *input, ITensorInfo *output, const Coordinates &starts, const Coordinates &ends);
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLSlice
+     *
+     * @note Supported tensor rank: up to 4
+     * @note Start indices must be non-negative. 0 <= starts[i]
+     * @note End coordinates can be negative, which represents the number of elements before the end of that dimension.
+     * @note End indices are not inclusive unless negative.
+     *
+     * @param[in] input  Source tensor info. Data type supported: All
+     * @param[in] output Destination tensor info. Data type supported: Same as @p input
+     * @param[in] starts The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
+     * @param[in] ends   The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
+     *
+     * @return A status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Coordinates &starts, const Coordinates &ends);
+};
+} // namespace experimental
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_CL_SLICE_H */
diff --git a/arm_compute/runtime/ITensorAllocator.h b/arm_compute/runtime/ITensorAllocator.h
index e80f7c4fb9..17e581b40e 100644
--- a/arm_compute/runtime/ITensorAllocator.h
+++ b/arm_compute/runtime/ITensorAllocator.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016-2019 Arm Limited.
+ * Copyright (c) 2016-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -36,7 +36,7 @@ class ITensorAllocator
 {
 public:
     /** Default constructor. */
-    ITensorAllocator();
+    ITensorAllocator() = default;
     /** Allow instances of this class to be copy constructed */
     ITensorAllocator(const ITensorAllocator &) = default;
     /** Allow instances of this class to be copied */
@@ -54,6 +54,14 @@ public:
      * @param[in] alignment Alignment in bytes that the underlying base pointer should comply with.
      */
     void init(const TensorInfo &input, size_t alignment = 0);
+    /** Initialize a tensor based with a reference TensorInfo
+     *
+     * @note ITensorAllocator won't own the TensorInfo thus these need to out-live
+     *
+     * @param[in] input     TensorInfo object containing the description of the tensor to initialize.
+     * @param[in] alignment Alignment in bytes that the underlying base pointer should comply with.
+     */
+    void soft_init(TensorInfo &input, size_t alignment = 0);
     /** Return a reference to the tensor's metadata
      *
      * @return Reference to the tensor's metadata.
@@ -93,8 +101,9 @@ protected:
     virtual void unlock() = 0;
 
 private:
-    TensorInfo _info;      /**< Tensor's metadata. */
-    size_t     _alignment; /**< Tensor's alignment in bytes */
+    TensorInfo  _info_owned{};             /**< Tensor's metadata. */
+    TensorInfo *_info_external{ nullptr }; /**< External Tensor's metadata */
+    size_t      _alignment{};              /**< Tensor's alignment in bytes */
 };
 } // namespace arm_compute
 #endif /*ARM_COMPUTE_ITENSORALLOCATOR_H */
diff --git a/arm_compute/runtime/NEON/functions/NESlice.h b/arm_compute/runtime/NEON/functions/NESlice.h
index 214ffa512c..550bfd2188 100644
--- a/arm_compute/runtime/NEON/functions/NESlice.h
+++ b/arm_compute/runtime/NEON/functions/NESlice.h
@@ -32,25 +32,44 @@ namespace arm_compute
 // Forward Declarations
 class ITensor;
 
-namespace experimental
-{
 /** Basic function to perform tensor slicing */
-class NESlice : public INEOperator
+class NESlice : public IFunction
 {
 public:
+    /** Default Constructor */
+    NESlice();
+    /** Default Destructor */
+    ~NESlice();
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NESlice(const NESlice &) = delete;
+    /** Default move constructor */
+    NESlice(NESlice &&);
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NESlice &operator=(const NESlice &) = delete;
+    /** Default move assignment operator */
+    NESlice &operator=(NESlice &&);
+
     /** Configure kernel
      *
+     * Valid data layouts:
+     * - All
+     *
+     * Valid data type configurations:
+     * |src    |dst    |
+     * |:------|:------|
+     * |All    |All    |
+     *
      * @note Supported tensor rank: up to 4
      * @note Start indices must be non-negative. 0 <= starts[i]
      * @note End coordinates can be negative, which represents the number of elements before the end of that dimension.
      * @note End indices are not inclusive unless negative.
      *
-     * @param[in]  input  Source tensor info. Data type supported: All
-     * @param[out] output Destination tensor info. Data type supported: Same as @p input
+     * @param[in]  input  Source tensor. Data type supported: All
+     * @param[out] output Destination tensor. Data type supported: Same as @p input
      * @param[in]  starts The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
      * @param[in]  ends   The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
      */
-    void configure(const ITensorInfo *input, ITensorInfo *output, const Coordinates &starts, const Coordinates &ends);
+    void configure(const ITensor *input, ITensor *output, const Coordinates &starts, const Coordinates &ends);
 
     /** Static function to check if given info will lead to a valid configuration of @ref NESlice
      *
@@ -67,26 +86,21 @@ public:
      * @return A status
      */
     static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Coordinates &starts, const Coordinates &ends);
+
+    // Inherited methods overridden:
+    void run() override;
+
+private:
+    struct Impl;
+    std::unique_ptr<Impl> _impl;
 };
-} // namespace experimental
 
+namespace experimental
+{
 /** Basic function to perform tensor slicing */
-class NESlice : public IFunction
+class NESlice : public INEOperator
 {
 public:
-    /** Default Constructor */
-    NESlice();
-    /** Default Destructor */
-    ~NESlice();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NESlice(const NESlice &) = delete;
-    /** Default move constructor */
-    NESlice(NESlice &&);
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NESlice &operator=(const NESlice &) = delete;
-    /** Default move assignment operator */
-    NESlice &operator=(NESlice &&);
-
     /** Configure kernel
      *
      * Valid data layouts:
@@ -102,12 +116,12 @@ public:
      * @note End coordinates can be negative, which represents the number of elements before the end of that dimension.
      * @note End indices are not inclusive unless negative.
      *
-     * @param[in]  input  Source tensor. Data type supported: All
-     * @param[out] output Destination tensor. Data type supported: Same as @p input
+     * @param[in]  input  Source tensor info. Data type supported: All
+     * @param[out] output Destination tensor info. Data type supported: Same as @p input
      * @param[in]  starts The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
      * @param[in]  ends   The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
      */
-    void configure(const ITensor *input, ITensor *output, const Coordinates &starts, const Coordinates &ends);
+    void configure(const ITensorInfo *input, ITensorInfo *output, const Coordinates &starts, const Coordinates &ends);
 
     /** Static function to check if given info will lead to a valid configuration of @ref NESlice
      *
@@ -124,13 +138,7 @@ public:
      * @return A status
      */
     static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Coordinates &starts, const Coordinates &ends);
-
-    // Inherited methods overridden:
-    void run() override;
-
-private:
-    struct Impl;
-    std::unique_ptr<Impl> _impl;
 };
+} // namespace experimental
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_NE_SLICE_H */
diff --git a/arm_compute/runtime/NEON/functions/NEStridedSlice.h b/arm_compute/runtime/NEON/functions/NEStridedSlice.h
index 7ba6a52a58..0b4c2a63a1 100644
--- a/arm_compute/runtime/NEON/functions/NEStridedSlice.h
+++ b/arm_compute/runtime/NEON/functions/NEStridedSlice.h
@@ -32,18 +32,37 @@ namespace arm_compute
 // Forward Declarations
 class ITensor;
 
-namespace experimental
-{
 /** Basic function to run @ref NEStridedSliceKernel */
-class NEStridedSlice : public INEOperator
+class NEStridedSlice : public IFunction
 {
 public:
+    /** Default Constructor */
+    NEStridedSlice();
+    /** Default Destructor */
+    ~NEStridedSlice();
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEStridedSlice(const NEStridedSlice &) = delete;
+    /** Default move constructor */
+    NEStridedSlice(NEStridedSlice &&);
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEStridedSlice &operator=(const NEStridedSlice &) = delete;
+    /** Default move assignment operator */
+    NEStridedSlice &operator=(NEStridedSlice &&);
+
     /** Configure kernel
      *
+     * Valid data layouts:
+     * - All
+     *
+     * Valid data type configurations:
+     * |src    |dst    |
+     * |:------|:------|
+     * |All    |All    |
+     *
      * @note Supported tensor rank: up to 4
      *
-     * @param[in]  input            Source tensor info. Data type supported: All
-     * @param[out] output           Destination tensor info. Data type supported: Same as @p input
+     * @param[in]  input            Source tensor. Data type supported: All
+     * @param[out] output           Destination tensor. Data type supported: Same as @p input
      * @param[in]  starts           The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
      * @param[in]  ends             The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
      * @param[in]  strides          The strides of the dimensions of the input tensor to be sliced. The length must be of rank(input).
@@ -52,7 +71,7 @@ public:
      * @param[in]  shrink_axis_mask (Optional) If the ith bit of shrink_axis_mask is set, it implies that the ith specification shrinks the dimensionality by 1.
      *                              A slice of size 1 starting from starts[i] in the dimension must be preserved.
      */
-    void configure(const ITensorInfo *input, ITensorInfo *output,
+    void configure(const ITensor *input, ITensor *output,
                    const Coordinates &starts, const Coordinates &ends, const BiStrides &strides,
                    int32_t begin_mask = 0, int32_t end_mask = 0, int32_t shrink_axis_mask = 0);
 
@@ -73,26 +92,21 @@ public:
     static Status validate(const ITensorInfo *input, const ITensorInfo *output,
                            const Coordinates &starts, const Coordinates &ends, const BiStrides &strides,
                            int32_t begin_mask = 0, int32_t end_mask = 0, int32_t shrink_axis_mask = 0);
+
+    // Inherited methods overridden:
+    void run() override;
+
+private:
+    struct Impl;
+    std::unique_ptr<Impl> _impl;
 };
-} // namespace experimental
 
+namespace experimental
+{
 /** Basic function to run @ref NEStridedSliceKernel */
-class NEStridedSlice : public IFunction
+class NEStridedSlice : public INEOperator
 {
 public:
-    /** Default Constructor */
-    NEStridedSlice();
-    /** Default Destructor */
-    ~NEStridedSlice();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NEStridedSlice(const NEStridedSlice &) = delete;
-    /** Default move constructor */
-    NEStridedSlice(NEStridedSlice &&);
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NEStridedSlice &operator=(const NEStridedSlice &) = delete;
-    /** Default move assignment operator */
-    NEStridedSlice &operator=(NEStridedSlice &&);
-
     /** Configure kernel
      *
      * Valid data layouts:
@@ -105,8 +119,8 @@ public:
      *
      * @note Supported tensor rank: up to 4
      *
-     * @param[in]  input            Source tensor. Data type supported: All
-     * @param[out] output           Destination tensor. Data type supported: Same as @p input
+     * @param[in]  input            Source tensor info. Data type supported: All
+     * @param[out] output           Destination tensor info. Data type supported: Same as @p input
      * @param[in]  starts           The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
      * @param[in]  ends             The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
      * @param[in]  strides          The strides of the dimensions of the input tensor to be sliced. The length must be of rank(input).
@@ -115,7 +129,7 @@ public:
      * @param[in]  shrink_axis_mask (Optional) If the ith bit of shrink_axis_mask is set, it implies that the ith specification shrinks the dimensionality by 1.
      *                              A slice of size 1 starting from starts[i] in the dimension must be preserved.
      */
-    void configure(const ITensor *input, ITensor *output,
+    void configure(const ITensorInfo *input, ITensorInfo *output,
                    const Coordinates &starts, const Coordinates &ends, const BiStrides &strides,
                    int32_t begin_mask = 0, int32_t end_mask = 0, int32_t shrink_axis_mask = 0);
 
@@ -136,13 +150,7 @@ public:
     static Status validate(const ITensorInfo *input, const ITensorInfo *output,
                            const Coordinates &starts, const Coordinates &ends, const BiStrides &strides,
                            int32_t begin_mask = 0, int32_t end_mask = 0, int32_t shrink_axis_mask = 0);
-
-    // Inherited methods overridden:
-    void run() override;
-
-private:
-    struct Impl;
-    std::unique_ptr<Impl> _impl;
 };
+} // namespace experimental
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_NE_STRIDED_SLICE_H */
diff --git a/docs/ComputeLibrary.dir b/docs/ComputeLibrary.dir
index 74ac9d9d23..e08f05eb2d 100644
--- a/docs/ComputeLibrary.dir
+++ b/docs/ComputeLibrary.dir
@@ -230,7 +230,7 @@
  *  @brief Scalar operations
  */
 
-/** @dir src/core/CL/gemm
+/** @dir src/core/gpu/cl/kernels/gemm
  *  @brief Folder containing all the configuration files for GEMM
  */
 
diff --git a/docs/user_guide/introduction.dox b/docs/user_guide/introduction.dox
index 25274958ba..6b10b9c2a2 100644
--- a/docs/user_guide/introduction.dox
+++ b/docs/user_guide/introduction.dox
@@ -99,7 +99,7 @@ This archive contains:
  - The latest Khronos EGL 1.5 C headers from the <a href="https://www.khronos.org/registry/gles/">Khronos EGL registry</a>
  - The sources for a stub version of libOpenCL.so, libGLESv1_CM.so, libGLESv2.so and libEGL.so to help you build your application.
  - An examples folder containing a few examples to compile and link against the library.
- - A @ref utils folder containing headers with some boiler plate code used by the examples.
+ - A utils folder containing headers with some boiler plate code used by the examples.
  - This documentation.
 
  For detailed information about file organization, please refer to Files -> File List section of this documentation.
diff --git a/docs/user_guide/release_version_and_change_log.dox b/docs/user_guide/release_version_and_change_log.dox
index 0f4d4cc0d5..a975e8b35e 100644
--- a/docs/user_guide/release_version_and_change_log.dox
+++ b/docs/user_guide/release_version_and_change_log.dox
@@ -280,7 +280,7 @@ v20.11 Public major release
    - CLLogits1DMaxShiftExpSumKernel
    - CLLogits1DNormKernel
    - CLHeightConcatenateLayerKernel
-   - @ref CLGEMMMatrixMultiplyKernel
+   - CLGEMMMatrixMultiplyKernel
    - @ref CLGEMMLowpQuantizeDownInt32ScaleKernel
    - @ref CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel
    - @ref CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel
@@ -567,14 +567,14 @@ v20.08 Public major release
       The default "axis" value for @ref NESoftmaxLayer, @ref NELogSoftmaxLayer is changed from 1 to 0.
       Only axis 0 is supported.
  - The support for quantized data types has been removed from @ref CLLogSoftmaxLayer due to implementation complexity.
- - Removed padding requirement for the input (e.g. LHS of GEMM) and output in @ref CLGEMMMatrixMultiplyNativeKernel, @ref CLGEMMMatrixMultiplyReshapedKernel, @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel and @ref CLIm2ColKernel (NHWC only)
+ - Removed padding requirement for the input (e.g. LHS of GEMM) and output in CLGEMMMatrixMultiplyNativeKernel, CLGEMMMatrixMultiplyReshapedKernel, CLGEMMMatrixMultiplyReshapedOnlyRHSKernel and @ref CLIm2ColKernel (NHWC only)
    - This change allows to use @ref CLGEMMConvolutionLayer without extra padding for the input and output.
    - Only the weights/bias of @ref CLGEMMConvolutionLayer could require padding for the computation.
-   - Only on Arm® Mali™ Midgard GPUs, @ref CLGEMMConvolutionLayer could require padding since @ref CLGEMMMatrixMultiplyKernel is called and currently requires padding.
- - Added support for exporting the OpenCL buffer object to the OpenCL image object in @ref CLGEMMMatrixMultiplyReshapedKernel and @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.
+   - Only on Arm® Mali™ Midgard GPUs, @ref CLGEMMConvolutionLayer could require padding since CLGEMMMatrixMultiplyKernel is called and currently requires padding.
+ - Added support for exporting the OpenCL buffer object to the OpenCL image object in CLGEMMMatrixMultiplyReshapedKernel and CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.
    - This support allows to export the OpenCL buffer used for the reshaped RHS matrix to the OpenCL image object.
-   - The padding requirement for the OpenCL image object is considered into the @ref CLGEMMReshapeRHSMatrixKernel.
-   - The reshaped RHS matrix stores the weights when GEMM is used to accelerate @ref CLGEMMConvolutionLayer.
+   - The padding requirement for the OpenCL image object is considered into the CLGEMMReshapeRHSMatrixKernel.
+   - The reshaped RHS matrix stores the weights when GEMM is used to accelerate CLGEMMConvolutionLayer.
 
 v20.05 Public major release
  - Various bug fixes.
@@ -739,7 +739,7 @@ v19.11 Public major release
  - Added QASYMM16 support for:
     - @ref CLBoundingBoxTransform
  - Added FP16 support for:
-    - @ref CLGEMMMatrixMultiplyReshapedKernel
+    - CLGEMMMatrixMultiplyReshapedKernel
  - Added new data type QASYMM8_PER_CHANNEL support for:
     - CLDequantizationLayer
     - @ref NEDequantizationLayer
@@ -749,7 +749,7 @@ v19.11 Public major release
     - @ref CLDepthwiseConvolutionLayer
     - @ref NEDepthwiseConvolutionLayer
  - Added FP16 mixed-precision support for:
-    - @ref CLGEMMMatrixMultiplyReshapedKernel
+    - CLGEMMMatrixMultiplyReshapedKernel
     - CLPoolingLayerKernel
  - Added FP32 and FP16 ELU activation for:
     - @ref CLActivationLayer
@@ -813,9 +813,9 @@ v19.08 Public major release
     - @ref CLSinLayer
     - CLBatchConcatenateLayerKernel
     - @ref CLDepthToSpaceLayerKernel / @ref CLDepthToSpaceLayer
-    - @ref CLGEMMLowpMatrixMultiplyNativeKernel
+    - CLGEMMLowpMatrixMultiplyNativeKernel
     - CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
-    - @ref CLGEMMMatrixMultiplyNativeKernel
+    - CLGEMMMatrixMultiplyNativeKernel
     - CLMeanStdDevNormalizationKernel /CLMeanStdDevNormalizationLayer
     - @ref CLSpaceToDepthLayerKernel / @ref CLSpaceToDepthLayer
  - New examples:
@@ -862,7 +862,7 @@ v19.05 Public major release
     - @ref CLFFTRadixStageKernel
     - @ref CLFFTScaleKernel
     - @ref CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel
-    - @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
+    - CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
     - CLHeightConcatenateLayerKernel
     - @ref CLDirectDeconvolutionLayer
     - @ref CLFFT1D
@@ -947,9 +947,9 @@ v19.02 Public major release
     - @ref CLRsqrtLayer
     - @ref CLExpLayer
     - CLElementWiseUnaryLayerKernel
-    - @ref CLGEMMReshapeLHSMatrixKernel
-    - @ref CLGEMMReshapeRHSMatrixKernel
-    - @ref CLGEMMMatrixMultiplyReshapedKernel
+    - CLGEMMReshapeLHSMatrixKernel
+    - CLGEMMReshapeRHSMatrixKernel
+    - CLGEMMMatrixMultiplyReshapedKernel
     - @ref CLRangeKernel / @ref CLRange
     - @ref CLUnstack
     - @ref CLGatherKernel / @ref CLGather
@@ -1369,7 +1369,7 @@ v17.03.1 First Major public release of the sources
 v17.03 Sources preview
  - New OpenCL kernels / functions:
    - CLGradientKernel, CLEdgeNonMaxSuppressionKernel, CLEdgeTraceKernel / CLCannyEdge
-   - GEMM refactoring + FP16 support: CLGEMMInterleave4x4Kernel, CLGEMMTranspose1xWKernel, @ref CLGEMMMatrixMultiplyKernel, CLGEMMMatrixAdditionKernel / @ref CLGEMM
+   - GEMM refactoring + FP16 support: CLGEMMInterleave4x4Kernel, CLGEMMTranspose1xWKernel, CLGEMMMatrixMultiplyKernel, CLGEMMMatrixAdditionKernel / @ref CLGEMM
    - CLGEMMMatrixAccumulateBiasesKernel / @ref CLFullyConnectedLayer
    - CLTransposeKernel / @ref CLTranspose
    - CLLKTrackerInitKernel, CLLKTrackerStage0Kernel, CLLKTrackerStage1Kernel, CLLKTrackerFinalizeKernel / CLOpticalFlow
diff --git a/examples/gemm_tuner/CommonGemmExampleOptions.cpp b/examples/gemm_tuner/CommonGemmExampleOptions.cpp
index f1306ccf5c..bee202b99e 100644
--- a/examples/gemm_tuner/CommonGemmExampleOptions.cpp
+++ b/examples/gemm_tuner/CommonGemmExampleOptions.cpp
@@ -39,7 +39,7 @@ using namespace utils;
     return os;
 }
 
-CommonGemmExampleOptions::CommonGemmExampleOptions(CommandLineParser &parser, DataType default_data_type)
+CommonGemmExampleOptions::CommonGemmExampleOptions(arm_compute::utils::CommandLineParser &parser, arm_compute::DataType default_data_type)
     : help(parser.add_option<ToggleOption>("help")),
       M(parser.add_positional_option<SimpleOption<size_t>>("M", 100)),
       N(parser.add_positional_option<SimpleOption<size_t>>("N", 100)),
diff --git a/examples/gemm_tuner/cl_gemm_native.cpp b/examples/gemm_tuner/cl_gemm_native.cpp
index 5a144dabf7..093935f716 100644
--- a/examples/gemm_tuner/cl_gemm_native.cpp
+++ b/examples/gemm_tuner/cl_gemm_native.cpp
@@ -32,7 +32,7 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "arm_compute/runtime/CL/CLTuner.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h"
 #include "tests/CL/Helper.h"
 #include "utils/Utils.h"
 #include "utils/command_line/CommandLineOptions.h"
@@ -41,6 +41,7 @@
 #include <cstdlib>
 
 using namespace arm_compute;
+using namespace arm_compute::opencl::kernels;
 using namespace utils;
 using namespace arm_compute::misc::shape_calculator;
 using namespace gemm_tuner;
@@ -122,8 +123,8 @@ GemmConfigs consume_gemm_configs(const GemmConfigOptions &options)
 }
 
 } // namespace
-// Create function for CLGEMMMatrixMultiplyNativeKernel
-using CLGEMMMatrixMultiplyNative = test::CLSynthetizeFunction<CLGEMMMatrixMultiplyNativeKernel>;
+// Create function for ClGemmMatrixMultiplyNativeKernel
+using CLGEMMMatrixMultiplyNative = test::CLSynthetizeOperator<ClGemmMatrixMultiplyNativeKernel>;
 
 class CLGEMMMatrixMultiplyNativeExample : public Example
 {
@@ -197,7 +198,7 @@ public:
 
         // Validate argments
         Status status{};
-        status = gemm.validate((&lhs)->info(), (&rhs)->info(), (&bias)->info(), (&dst)->info(), alpha, beta, lhs_info, rhs_info, kernel_info);
+        status = gemm.validate(lhs.info(), rhs.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
         if(!status)
         {
             // Unsupported arguments
@@ -207,7 +208,7 @@ public:
         }
 
         // Configure function
-        gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        gemm.configure(lhs.info(), rhs.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         // Allocate tensors
         lhs.allocator()->allocate();
@@ -220,7 +221,12 @@ public:
     void do_run() override
     {
         // Execute the function
-        gemm.run();
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         // Make sure all the OpenCL jobs are done executing:
         CLScheduler::get().sync();
diff --git a/examples/gemm_tuner/cl_gemm_reshaped.cpp b/examples/gemm_tuner/cl_gemm_reshaped.cpp
index 444a342d74..e6caeec873 100644
--- a/examples/gemm_tuner/cl_gemm_reshaped.cpp
+++ b/examples/gemm_tuner/cl_gemm_reshaped.cpp
@@ -33,8 +33,8 @@
 #include "arm_compute/runtime/CL/CLTuner.h"
 #include "examples/gemm_tuner/CommonGemmExampleOptions.h"
 #include "examples/gemm_tuner/GemmTunerHelpers.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
 #include "tests/CL/Helper.h"
 #include "utils/Utils.h"
 #include "utils/command_line/CommandLineOptions.h"
@@ -43,6 +43,7 @@
 #include <cstdlib>
 
 using namespace arm_compute;
+using namespace arm_compute::opencl::kernels;
 using namespace utils;
 using namespace arm_compute::misc::shape_calculator;
 using namespace gemm_tuner;
@@ -172,10 +173,11 @@ GemmConfigs consume_gemm_configs(const GemmConfigOptions &options)
 }
 
 } // namespace
-// Create function for CLGEMMReshapeLHSMatrixKernel
-using CLGEMMReshapeLHSMatrix = test::CLSynthetizeFunction<CLGEMMReshapeLHSMatrixKernel>;
-// Create function for CLGEMMMatrixMultiplyReshapedKernel
-using CLGEMMMatrixMultiplyReshaped = test::CLSynthetizeFunction<CLGEMMMatrixMultiplyReshapedKernel>;
+
+// Create function for ClGemmReshapeLhsMatrixKernel
+using CLGEMMReshapeLHSMatrix = test::CLSynthetizeOperator<ClGemmReshapeLhsMatrixKernel>;
+// Create function for ClGemmMatrixMultiplyReshapedKernel
+using CLGEMMMatrixMultiplyReshaped = test::CLSynthetizeOperator<ClGemmMatrixMultiplyReshapedKernel>;
 
 class CLGEMMMatrixMultiplyReshapedExample : public Example
 {
@@ -271,7 +273,7 @@ public:
 
         // Validate argments
         Status status{};
-        status = reshape_lhs.validate((&lhs)->info(), (&lhs_reshaped)->info(), lhs_info, kernel_info.reinterpret_input_as_3d);
+        status = reshape_lhs.validate(lhs.info(), lhs_reshaped.info(), lhs_info, kernel_info.reinterpret_input_as_3d);
         if(!status)
         {
             // Unsupported arguments
@@ -280,7 +282,7 @@ public:
             return false;
         }
 
-        status = gemm.validate((&lhs_reshaped)->info(), (&rhs_reshaped)->info(), (&bias)->info(), (&dst)->info(), alpha, beta, lhs_info, rhs_info, kernel_info);
+        status = gemm.validate(lhs_reshaped.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
         if(!status)
         {
             // Unsupported arguments
@@ -290,10 +292,10 @@ public:
         }
 
         // Configure reshape lhs function
-        reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
+        reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
 
         // Configure function
-        gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        gemm.configure(lhs_reshaped.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         // Allocate tensors
         lhs.allocator()->allocate();
@@ -307,9 +309,16 @@ public:
     }
     void do_run() override
     {
-        // Execute the function
-        reshape_lhs.run();
-        gemm.run();
+        // Execute the functions
+        ITensorPack reshape_lsh_pack({ { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } });
+        reshape_lhs.run(reshape_lsh_pack);
+
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        reshape_lhs.run(gemm_pack);
 
         // Make sure all the OpenCL jobs are done executing:
         CLScheduler::get().sync();
diff --git a/examples/gemm_tuner/cl_gemm_reshaped_rhs_only.cpp b/examples/gemm_tuner/cl_gemm_reshaped_rhs_only.cpp
index 68bec9da6e..dbaaca6048 100644
--- a/examples/gemm_tuner/cl_gemm_reshaped_rhs_only.cpp
+++ b/examples/gemm_tuner/cl_gemm_reshaped_rhs_only.cpp
@@ -33,7 +33,7 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "arm_compute/runtime/CL/CLTuner.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h"
 #include "tests/CL/Helper.h"
 #include "utils/Utils.h"
 #include "utils/command_line/CommandLineOptions.h"
@@ -42,6 +42,7 @@
 #include <cstdlib>
 
 using namespace arm_compute;
+using namespace arm_compute::opencl::kernels;
 using namespace utils;
 using namespace arm_compute::misc::shape_calculator;
 using namespace gemm_tuner;
@@ -147,8 +148,8 @@ GemmConfigs consume_gemm_configs(const GemmConfigOptions &options)
 }
 
 } // namespace
-// Create function for CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-using CLGEMMMatrixMultiplyReshapedOnlyRHS = test::CLSynthetizeFunction<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>;
+// Create function for ClGemmMatrixMultiplyReshapedOnlyRhsKernel
+using CLGEMMMatrixMultiplyReshapedOnlyRHS = test::CLSynthetizeOperator<ClGemmMatrixMultiplyReshapedOnlyRhsKernel>;
 
 class CLGEMMMatrixMultiplyReshapedOnlyRHSExample : public Example
 {
@@ -238,7 +239,7 @@ public:
 
         // Validate argments
         Status status{};
-        status = gemm.validate((&lhs)->info(), (&rhs_reshaped)->info(), (&bias)->info(), (&dst)->info(), alpha, beta, lhs_info, rhs_info, kernel_info);
+        status = gemm.validate(lhs.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
         if(!status)
         {
             // Unsupported arguments
@@ -248,7 +249,7 @@ public:
         }
 
         // Configure function
-        gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        gemm.configure(lhs.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         // Allocate tensors
         lhs.allocator()->allocate();
@@ -262,7 +263,12 @@ public:
     void do_run() override
     {
         // Execute the function
-        gemm.run();
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         // Make sure all the OpenCL jobs are done executing:
         CLScheduler::get().sync();
diff --git a/examples/gemm_tuner/cl_gemmlowp_reshaped.cpp b/examples/gemm_tuner/cl_gemmlowp_reshaped.cpp
index 5b81963752..3d3f7fef1e 100644
--- a/examples/gemm_tuner/cl_gemmlowp_reshaped.cpp
+++ b/examples/gemm_tuner/cl_gemmlowp_reshaped.cpp
@@ -34,7 +34,7 @@
 #include "examples/gemm_tuner/CommonGemmExampleOptions.h"
 #include "examples/gemm_tuner/GemmTunerHelpers.h"
 #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
 #include "tests/CL/Helper.h"
 #include "utils/Utils.h"
 #include "utils/command_line/CommandLineOptions.h"
@@ -43,6 +43,7 @@
 #include <cstdlib>
 
 using namespace arm_compute;
+using namespace arm_compute::opencl::kernels;
 using namespace utils;
 using namespace arm_compute::misc::shape_calculator;
 using namespace gemm_tuner;
@@ -167,7 +168,7 @@ GemmConfigs consume_gemm_configs(const GemmConfigOptions &options)
 
 } // namespace
 
-using CLGEMMReshapeLHSMatrix           = test::CLSynthetizeFunction<CLGEMMReshapeLHSMatrixKernel>;
+using CLGEMMReshapeLHSMatrix           = test::CLSynthetizeOperator<ClGemmReshapeLhsMatrixKernel>;
 using CLGEMMLowpMatrixMultiplyReshaped = test::CLSynthetizeFunction<CLGEMMLowpMatrixMultiplyReshapedKernel>;
 
 class CLGEMMLowpMatrixMultiplyReshapedExample : public Example
@@ -279,7 +280,7 @@ public:
         }
 
         // Configure functions
-        reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
+        reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
 
         gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, gemm_info);
 
@@ -294,7 +295,9 @@ public:
     }
     void do_run() override
     {
-        reshape_lhs.run();
+        ITensorPack reshape_lsh_pack({ { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } });
+        reshape_lhs.run(reshape_lsh_pack);
+
         gemm.run();
 
         // Make sure all the OpenCL jobs are done executing:
diff --git a/src/core/CL/CLKernels.h b/src/core/CL/CLKernels.h
index 63978cea3f..1302d52180 100644
--- a/src/core/CL/CLKernels.h
+++ b/src/core/CL/CLKernels.h
@@ -54,12 +54,6 @@
 #include "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/CL/kernels/CLGatherKernel.h"
 #include "src/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
 #include "src/core/CL/kernels/CLIm2ColKernel.h"
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h
index 100100b1b1..06a73f173d 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -32,7 +32,9 @@ class ICLTensor;
 
 /** OpenCL kernel to multiply matrices when both the input matrices LHS (input0) and RHS (input1) have been reshaped
  *
- * @note The input matrices @p input0 and @p input1 must be reshaped through @ref CLGEMMReshapeLHSMatrixKernel and  @ref CLGEMMReshapeRHSMatrixKernel
+ * @note The input matrices @p input0 and @p input1 must be reshaped through:
+ *  - @ref opencl::kernels::ClGemmReshapeLhsMatrixKernel
+ *  - @ref opencl::kernels::ClGemmReshapeRhsMatrixKernel
  */
 class CLGEMMLowpMatrixMultiplyReshapedKernel : public ICLKernel
 {
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h
index 222a8615e4..e79f6dfe05 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -33,7 +33,7 @@ class ICLTensor;
 
 /** OpenCL kernel to multiply matrices with QASYMM8 data type when only the input matrix RHS (input1) has been reshaped
  *
- * @note The input matrix input1 must be reshaped through @ref CLGEMMReshapeRHSMatrixKernel
+ * @note The input matrix input1 must be reshaped through @ref opencl::kernels::ClGemmReshapeRhsMatrixKernel
  * @note For fused output stage, only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT type is supported
  */
 class CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel : public ICLKernel
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h
deleted file mode 100644
index 71d223b8ac..0000000000
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h
+++ /dev/null
@@ -1,122 +0,0 @@
-/*
- * Copyright (c) 2017-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLGEMMMATRIXMULTIPLYKERNEL_H
-#define ARM_COMPUTE_CLGEMMMATRIXMULTIPLYKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** OpenCL kernel to multiply two input matrices "A" and "B" and add a martix "C" if provided. All elements of the output matrix will be multiplied by alpha. In case matrix C is passed, it will be added to the previous result.
- *  For the matrix C, the broadcast addition is supported if the flag "broadcast_bias" is set in the GEMMReshapeInfo object
- *
- * @note If the input tensors @p input0 and @p input1 have been reshaped respectively with @ref CLGEMMReshapeLHSMatrixKernel" and @ref CLGEMMReshapeRHSMatrixKernel,
- *       the flag @p is_interleaved_transposed must be set to true
- *
- * @attention @p input1 tensor must have at least 2 dimensions (matrix)
- *
- */
-class CLGEMMMatrixMultiplyKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLGEMMMatrixMultiplyKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMMatrixMultiplyKernel(const CLGEMMMatrixMultiplyKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMMatrixMultiplyKernel &operator=(const CLGEMMMatrixMultiplyKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLGEMMMatrixMultiplyKernel(CLGEMMMatrixMultiplyKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLGEMMMatrixMultiplyKernel &operator=(CLGEMMMatrixMultiplyKernel &&) = default;
-    /** Initialise the kernel's input, output and alpha
-     *
-     * @param[in]  input0                    Input tensor containing the Matrix A. Data types supported: F16/F32
-     * @param[in]  input1                    Input tensor containing the Matrix B. Data type supported: same as @p input0
-     * @param[in]  input2                    Input tensor containing the Matrix C (bias). Can be nullptr. Data type supported: same as @p input0
-     * @param[out] output                    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
-     * @param[in]  alpha                     Weight of the matrix product
-     * @param[in]  beta                      (Optional) Weight of vector C. Default value is 0. Only beta = 1 is currently supported.
-     * @param[in]  is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel
-     * @param[in]  reshape_info              (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
-     * @param[in]  fp_mixed_precision        (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy
-     * @param[in]  activation_info           (Optional) Activation to apply after the matrix multiplication
-     *
-     */
-    void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta = 0.f,
-                   bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo());
-    /** Initialise the kernel's input, output and alpha
-     *
-     * @param[in]  compile_context           The compile context to be used.
-     * @param[in]  input0                    Input tensor containing the Matrix A. Data types supported: F16/F32
-     * @param[in]  input1                    Input tensor containing the Matrix B. Data type supported: same as @p input0
-     * @param[in]  input2                    Input tensor containing the Matrix C (bias). Can be nullptr. Data type supported: same as @p input0
-     * @param[out] output                    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
-     * @param[in]  alpha                     Weight of the matrix product
-     * @param[in]  beta                      (Optional) Weight of vector C. Default value is 0. Only beta = 1 is currently supported.
-     * @param[in]  is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel
-     * @param[in]  reshape_info              (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
-     * @param[in]  fp_mixed_precision        (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy
-     * @param[in]  activation_info           (Optional) Activation to apply after the matrix multiplication
-     *
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta = 0.f,
-                   bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo());
-    /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyKernel
-     *
-     * @param[in] input0                    Input tensor containing the Matrix A info. Data types supported: F16/F32
-     * @param[in] input1                    Input tensor containing the Matrix B info. Data type supported: same as @p input0
-     * @param[in] input2                    Input tensor containing the Matrix C (bias) info. Can be nullptr. Data type supported: same as @p input0
-     * @param[in] output                    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
-     * @param[in] alpha                     Weight of the matrix product
-     * @param[in] beta                      Weight of vector C. Default value is 0. Only beta = 1 is currently supported.
-     * @param[in] is_interleaved_transposed True if input0 and input1 have been reshaped respectively using @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel
-     * @param[in] reshape_info              GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
-     * @param[in] gpu_target                GPU Target
-     * @param[in] fp_mixed_precision        (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy
-     * @param[in] activation_info           (Optional) Activation to apply after the matrix multiplication
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
-                           bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo());
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-
-public:
-    const ICLTensor *_input0;
-    const ICLTensor *_input1;
-    const ICLTensor *_input2;
-    ICLTensor       *_output;
-    bool             _slide_matrix_b;
-    bool             _reinterpret_input_as_3d;
-    bool             _reinterpret_output_as_3d;
-    bool             _add_bias;
-    bool             _broadcast_bias;
-};
-} // namespace arm_compute
-#endif /* ARM_COMPUTE_CLGEMMMATRIXMULTIPLYKERNEL_H */
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h
deleted file mode 100644
index 6b6004b464..0000000000
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * Copyright (c) 2019-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H
-#define ARM_COMPUTE_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-#include "arm_compute/core/KernelDescriptors.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** OpenCL kernel to multiply matrices when neither of the input matrices have been reshaped */
-class CLGEMMMatrixMultiplyNativeKernel : public ICLKernel
-{
-public:
-    /** Default Constructor */
-    CLGEMMMatrixMultiplyNativeKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMMatrixMultiplyNativeKernel(const CLGEMMMatrixMultiplyNativeKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMMatrixMultiplyNativeKernel &operator=(const CLGEMMMatrixMultiplyNativeKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLGEMMMatrixMultiplyNativeKernel(CLGEMMMatrixMultiplyNativeKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLGEMMMatrixMultiplyNativeKernel &operator=(CLGEMMMatrixMultiplyNativeKernel &&) = default;
-    /** Initialise the kernel's input and output.
-     *
-     * @param[in]  input0    Input tensor for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4.
-     * @param[in]  input1    Input tensor for the RHS matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3.
-     * @param[in]  input2    Input tensor containing the bias matrix. Data type supported: same as @p input0.
-     * @param[out] output    Output tensor info. Data type supported: same as @p input0
-     * @param[in]  alpha     Weight of the matrix product
-     * @param[in]  beta      Weight of the matrix bias
-     * @param[in]  lhs_info  LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported:
-     *                       lhs_info.m0: 1,2,3,4,5,6,7,8
-     *                       lhs_info.k0: 2,3,4,8,16
-     * @param[in]  rhs_info  RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported:
-     *                       rhs_info.n0: 2,3,4,8,16
-     *                       rhs_info.k0: same of lhs_info.k0
-     * @param[in]  gemm_info GEMM information used to retrieve the original dimensions of the input matrices
-     */
-    void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
-                   const GEMMRHSMatrixInfo &rhs_info,
-                   const GEMMKernelInfo    &gemm_info);
-    /** Initialise the kernel's input and output.
-     *
-     * @param[in]  compile_context The compile context to be used.
-     * @param[in]  input0          Input tensor for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4.
-     * @param[in]  input1          Input tensor for the RHS matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3.
-     * @param[in]  input2          Input tensor containing the bias matrix. Data type supported: same as @p input0.
-     * @param[out] output          Output tensor info. Data type supported: same as @p input0
-     * @param[in]  alpha           Weight of the matrix product
-     * @param[in]  beta            Weight of the matrix bias
-     * @param[in]  lhs_info        LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported:
-     *                             lhs_info.m0: 1,2,3,4,5,6,7,8
-     *                             lhs_info.k0: 2,3,4,8,16
-     * @param[in]  rhs_info        RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported:
-     *                             rhs_info.n0: 2,3,4,8,16
-     *                             rhs_info.k0: same of lhs_info.k0
-     * @param[in]  gemm_info       GEMM information used to retrieve the original dimensions of the input matrices
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
-                   const GEMMLHSMatrixInfo &lhs_info,
-                   const GEMMRHSMatrixInfo &rhs_info,
-                   const GEMMKernelInfo    &gemm_info);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyNativeKernel
-     *
-     * @param[in] input0    Input tensor info for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4.
-     * @param[in] input1    Input tensor info for the RHS matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3.
-     * @param[in] input2    Input tensor info containing the bias matrix. Data type supported: same as @p input0.
-     * @param[in] output    Output tensor info. Data type supported: same as @p input0
-     * @param[in] alpha     Weight of the matrix product
-     * @param[in] beta      Weight of the matrix bias
-     * @param[in] lhs_info  LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported:
-     *                      lhs_info.m0: 1,2,3,4,5,6,7,8
-     *                      lhs_info.k0: 2,3,4,8,16
-     * @param[in] rhs_info  RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported:
-     *                      rhs_info.n0: 2,3,4,8,16
-     *                      rhs_info.k0: same of lhs_info.k0
-     * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
-                           const GEMMRHSMatrixInfo &rhs_info,
-                           const GEMMKernelInfo    &gemm_info);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-
-private:
-    const ICLTensor *_input0;
-    const ICLTensor *_input1;
-    const ICLTensor *_input2;
-    ICLTensor       *_output;
-    bool             _slide_matrix_b;
-    bool             _reinterpret_input_as_3d;
-    bool             _reinterpret_output_as_3d;
-    bool             _use_dummy_work_items;
-    bool             _add_bias;
-    bool             _broadcast_bias;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H*/
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h
deleted file mode 100644
index 2ffc322def..0000000000
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h
+++ /dev/null
@@ -1,188 +0,0 @@
-/*
- * Copyright (c) 2018-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDKERNEL_H
-#define ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-#include "arm_compute/core/KernelDescriptors.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** OpenCL kernel to multiply matrices when both the input matrices LHS (input0) and RHS (input1) have been reshaped
- *
- * @note The input matrices @p input0 and @p input1 must be reshaped through @ref CLGEMMReshapeLHSMatrixKernel and  @ref CLGEMMReshapeRHSMatrixKernel
- */
-class CLGEMMMatrixMultiplyReshapedKernel : public ICLKernel
-{
-public:
-    /** Default Constructor */
-    CLGEMMMatrixMultiplyReshapedKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMMatrixMultiplyReshapedKernel(const CLGEMMMatrixMultiplyReshapedKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMMatrixMultiplyReshapedKernel &operator=(const CLGEMMMatrixMultiplyReshapedKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLGEMMMatrixMultiplyReshapedKernel(CLGEMMMatrixMultiplyReshapedKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLGEMMMatrixMultiplyReshapedKernel &operator=(CLGEMMMatrixMultiplyReshapedKernel &&) = default;
-    /** Initialise the kernel's input and output.
-     *
-     * @note The F16 computation also supports mixed precision through the gemm_info.fp_mixed_precision flag.
-     *       Mixed precision combines different floating precisions during the computation, in particular, F32 for the accumulations and F16 for the
-     *       multiplications. i.e. float c = (half)a * (half)b
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function.
-     *       Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer,
-     *       the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement
-     *       -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *
-     * @param[in]  input0    Input tensor containing the LHS reshaped matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). The number of dimensions for the LHS matrix must be less or equal than 4
-     * @param[in]  input1    Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3
-     * @param[in]  input2    Input tensor containing the bias matrix. Data type supported: same as @p input0.
-     * @param[out] output    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
-     * @param[in]  alpha     Weight of the matrix product
-     * @param[in]  beta      Weight of the matrix bias
-     * @param[in]  lhs_info  LHS matrix information used for reshaping the input0 tensor. Only the following values are supported:
-     *                       lhs_info.m0: 2,3,4,5,6,7,8
-     *                       lhs_info.k0: 2,3,4,8,16
-     *                       lhs_info.transpose: false
-     * @param[in]  rhs_info  RHS matrix information used for reshaping the input1 tensor.  Only the following values are supported:
-     *                       rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true)
-     *                       rhs_info.k0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true)
-     *                       rhs_info.transpose: true
-     * @param[in]  gemm_info GEMM information used to retrieve the original dimensions of the input matrices
-     *
-     * @note lhs_info.k0 must be equal to rhs_info.k0
-     */
-    void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
-                   const GEMMRHSMatrixInfo &rhs_info,
-                   const GEMMKernelInfo    &gemm_info);
-    /** Initialise the kernel's input and output.
-     *
-     * @note The F16 computation also supports mixed precision through the gemm_info.fp_mixed_precision flag.
-     *       Mixed precision combines different floating precisions during the computation, in particular, F32 for the accumulations and F16 for the
-     *       multiplications. i.e. float c = (half)a * (half)b
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function.
-     *       Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer,
-     *       the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement
-     *       -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *
-     * @param[in]  compile_context The compile context to be used.
-     * @param[in]  input0          Input tensor containing the LHS reshaped matrix. Data type supported: F16/F32  (only F32 if rhs_info.export_to_cl_image = true). The number of dimensions for the LHS matrix must be less or equal than 4
-     * @param[in]  input1          Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3
-     * @param[in]  input2          Input tensor containing the bias matrix. Data type supported: same as @p input0.
-     * @param[out] output          Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
-     * @param[in]  alpha           Weight of the matrix product
-     * @param[in]  beta            Weight of the matrix bias
-     * @param[in]  lhs_info        LHS matrix information used for reshaping the input0 tensor.  Only the following values are supported:
-     *                             lhs_info.m0: 2,3,4,5,6,7,8
-     *                             lhs_info.k0: 2,3,4,8,16
-     *                             lhs_info.transpose: false
-     * @param[in]  rhs_info        RHS matrix information used for reshaping the input1 tensor.  Only the following values are supported:
-     *                             rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true)
-     *                             rhs_info.k0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true)
-     *                             rhs_info.transpose: true
-     * @param[in]  gemm_info       GEMM information used to retrieve the original dimensions of the input matrices
-     *
-     * @note lhs_info.k0 must be equal to rhs_info.k0
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
-                   const GEMMLHSMatrixInfo &lhs_info,
-                   const GEMMRHSMatrixInfo &rhs_info,
-                   const GEMMKernelInfo    &gemm_info);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyReshapedKernel
-     *
-     * @note The F16 computation also supports mixed precision through the gemm_info.fp_mixed_precision flag.
-     *       Mixed precision combines different floating precisions during the computation, in particular, F32 for the accumulations and F16 for the
-     *       multiplications. i.e. float c = (half)a * (half)b
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function.
-     *       Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer,
-     *       the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement
-     *       -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *
-     * @param[in] input0    Input tensor containing the LHS reshaped matrix. Data type supported: F16/F32  (only F32 if rhs_info.export_to_cl_image = true). The number of dimensions for the LHS matrix must be less or equal than 4
-     * @param[in] input1    Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3
-     * @param[in] input2    Input tensor info containing the bias matrix. Data type supported: same as @p input0.
-     * @param[in] output    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
-     * @param[in] alpha     Weight of the matrix product
-     * @param[in] beta      Weight of the matrix bias
-     * @param[in] lhs_info  LHS matrix information used for reshaping the input0 tensor.  Only the following values are supported:
-     *                      lhs_info.m0: 2,3,4,5,6,7,8
-     *                      lhs_info.k0: 2,3,4,8,16
-     *                      lhs_info.transpose: false
-     * @param[in] rhs_info  RHS matrix information used for reshaping the input1 tensor.  Only the following values are supported:
-     *                      rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true)
-     *                      rhs_info.k0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true)
-     *                      rhs_info.transpose: true
-     * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices
-     *
-     * @note lhs_info.k0 must be equal to rhs_info.k0
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
-                           const GEMMRHSMatrixInfo &rhs_info,
-                           const GEMMKernelInfo    &gemm_info);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-
-private:
-    const ICLTensor *_input0;
-    const ICLTensor *_input1;
-    const ICLTensor *_input2;
-    ICLTensor       *_output;
-    bool             _slide_matrix_b;
-    bool             _reinterpret_output_as_3d;
-    bool             _use_dummy_work_items;
-    bool             _add_bias;
-    bool             _broadcast_bias;
-    bool             _export_to_cl_image;
-    unsigned int     _k;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDKERNEL_H*/
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h
deleted file mode 100644
index 5b96679a46..0000000000
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h
+++ /dev/null
@@ -1,168 +0,0 @@
-/*
- * Copyright (c) 2019-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDONLYRHSKERNEL_H
-#define ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDONLYRHSKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-#include "arm_compute/core/KernelDescriptors.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** OpenCL kernel to multiply matrices when only the input matrix RHS (input1) has been reshaped
- *
- * @note The input matrix input1 must be reshaped through @ref CLGEMMReshapeRHSMatrixKernel
- */
-class CLGEMMMatrixMultiplyReshapedOnlyRHSKernel : public ICLKernel
-{
-public:
-    /** Default Constructor */
-    CLGEMMMatrixMultiplyReshapedOnlyRHSKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMMatrixMultiplyReshapedOnlyRHSKernel(const CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &operator=(const CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLGEMMMatrixMultiplyReshapedOnlyRHSKernel(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &operator=(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &&) = default;
-    /** Initialise the kernel's input and output.
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function.
-     *       Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer,
-     *       the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement
-     *       -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *
-     * @param[in]  input0    Input tensor containing the LHS matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true).
-     *                       The number of dimensions for the LHS matrix must be less or equal than 4.
-     * @param[in]  input1    Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3.
-     * @param[in]  input2    Input tensor containing the bias matrix. Data type supported: same as @p input0.
-     * @param[out] output    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
-     * @param[in]  alpha     Weight of the matrix product
-     * @param[in]  beta      Weight of the matrix bias
-     * @param[in]  lhs_info  LHS matrix information used to retrieve the number of rows to be processed by each thread. Only the following values are supported:
-     *                       lhs_info.m0: 1,2,3,4,5,6,7,8
-     * @param[in]  rhs_info  RHS matrix information used for reshaping the input1 tensor.  Only the following values are supported:
-     *                       rhs_info.k0: 2,3,4,8,16
-     *                       rhs_info.n0: 2,3,4,8,16
-     *                       rhs_info.transpose: true,false
-     * @param[in]  gemm_info GEMM information used to retrieve the original dimensions of the input matrices
-     */
-    void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
-                   const GEMMRHSMatrixInfo &rhs_info,
-                   const GEMMKernelInfo    &gemm_info);
-    /** Initialise the kernel's input and output.
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function.
-     *       Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer,
-     *       the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement
-     *       -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *
-     * @param[in]  compile_context The compile context to be used.
-     * @param[in]  input0          Input tensor containing the LHS matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true).
-     *                             The number of dimensions for the LHS matrix must be less or equal than 4.
-     * @param[in]  input1          Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3.
-     * @param[in]  input2          Input tensor containing the bias matrix. Data type supported: same as @p input0.
-     * @param[out] output          Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
-     * @param[in]  alpha           Weight of the matrix product
-     * @param[in]  beta            Weight of the matrix bias
-     * @param[in]  lhs_info        LHS matrix information used to retrieve the number of rows to be processed by each thread. Only the following values are supported:
-     *                             lhs_info.m0: 1,2,3,4,5,6,7,8
-     * @param[in]  rhs_info        RHS matrix information used for reshaping the input1 tensor.  Only the following values are supported:
-     *                             rhs_info.k0: 2,3,4,8,16
-     *                             rhs_info.n0: 2,3,4,8,16
-     *                             rhs_info.transpose: true,false
-     * @param[in]  gemm_info       GEMM information used to retrieve the original dimensions of the input matrices
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
-                   const GEMMLHSMatrixInfo &lhs_info,
-                   const GEMMRHSMatrixInfo &rhs_info,
-                   const GEMMKernelInfo    &gemm_info);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function.
-     *       Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer,
-     *       the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement
-     *       -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *
-     * @param[in] input0    Input tensor info for the LHS matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true).
-     *                      The number of dimensions for the LHS matrix must be less or equal than 4.
-     * @param[in] input1    Input tensor info for the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3.
-     * @param[in] input2    Input tensor info containing the bias matrix. Data type supported: same as @p input0.
-     * @param[in] output    Output tensor info. Data type supported: same as @p input0
-     * @param[in] alpha     Weight of the matrix product
-     * @param[in] beta      Weight of the matrix bias
-     * @param[in] lhs_info  LHS matrix information used to retrieve the number of rows to be processed by each thread. Only the following values are supported:
-     *                      lhs_info.m0: 1,2,3,4,5,6,7,8
-     * @param[in] rhs_info  RHS matrix information used for reshaping the input1 tensor.  Only the following values are supported:
-     *                      rhs_info.k0: 2,3,4,8,16
-     *                      rhs_info.n0: 2,3,4,8,16
-     *                      rhs_info.transpose: true,false
-     * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
-                           const GEMMRHSMatrixInfo &rhs_info,
-                           const GEMMKernelInfo    &gemm_info);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-
-private:
-    const ICLTensor *_input0;
-    const ICLTensor *_input1;
-    const ICLTensor *_input2;
-    ICLTensor       *_output;
-    bool             _slide_matrix_b;
-    bool             _reinterpret_input_as_3d;
-    bool             _reinterpret_output_as_3d;
-    bool             _use_dummy_work_items;
-    bool             _add_bias;
-    bool             _broadcast_bias;
-    bool             _export_to_cl_image;
-    bool             _has_pad_y;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDONLYRHSKERNEL_H*/
diff --git a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h b/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h
deleted file mode 100644
index 92202a26fc..0000000000
--- a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Copyright (c) 2018-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLGEMMRESHAPELHSMATRIXKERNEL_H
-#define ARM_COMPUTE_CLGEMMRESHAPELHSMATRIXKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** OpenCL kernel to reshape the LHS matrix when performing the matrix multiplication.
- *  In particular, this function splits the input matrix in blocks of size M0xK0 (defined through GEMMLHSInfo) and
- *  stores each one in the output matrix unrolling the values
- */
-class CLGEMMReshapeLHSMatrixKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLGEMMReshapeLHSMatrixKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMReshapeLHSMatrixKernel(const CLGEMMReshapeLHSMatrixKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMReshapeLHSMatrixKernel &operator=(const CLGEMMReshapeLHSMatrixKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLGEMMReshapeLHSMatrixKernel(CLGEMMReshapeLHSMatrixKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLGEMMReshapeLHSMatrixKernel &operator=(CLGEMMReshapeLHSMatrixKernel &&) = default;
-    /** Initialise the kernel's input and output.
-     *
-     * @param[in]  input                   Input tensor. Data types supported: All
-     * @param[out] output                  Output tensor. Data type supported: same as @p input
-     * @param[in]  lhs_info                LHS matrix information to be used for reshaping. This object contains all the necessary
-     *                                     information to reshape the input tensor. Only the following values are supported:
-     *                                     lhs_info.m0: 2,3,4,5,6,7,8
-     *                                     lhs_info.k0: 2,3,4,8,16
-     *                                     lhs_info.v0: greater than 0
-     *                                     lhs_info.transpose: true, false
-     *                                     lhs_info.interleave: true, false
-     * @param[in]  reinterpret_input_as_3d (Optional) True if the input has to be reinterpreted as 3D tensor
-     */
-    void configure(const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d = false);
-    /** Initialise the kernel's input and output.
-     *
-     * @param[in]  compile_context         The compile context to be used.
-     * @param[in]  input                   Input tensor. Data types supported: All
-     * @param[out] output                  Output tensor. Data type supported: same as @p input
-     * @param[in]  lhs_info                LHS matrix information to be used for reshaping. This object contains all the necessary
-     *                                     information to reshape the input tensor. Only the following values are supported:
-     *                                     lhs_info.m0: 2,3,4,5,6,7,8
-     *                                     lhs_info.k0: 2,3,4,8,16
-     *                                     lhs_info.v0: greater than 0
-     *                                     lhs_info.transpose: true, false
-     *                                     lhs_info.interleave: true, false
-     * @param[in]  reinterpret_input_as_3d (Optional) True if the input has to be reinterpreted as 3D tensor
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d = false);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMReshapeLHSMatrixKernel
-     *
-     * @param[in] input                   Input tensor info. Data types supported: All
-     * @param[in] output                  Output tensor info which stores the interleaved matrix. Data type supported: same as @p input.
-     * @param[in] lhs_info                LHS matrix information to be used for reshaping. This object contains all the necessary
-     *                                    information to reshape the input tensor. Only the following values are supported:
-     *                                    lhs_info.m0: 2,3,4,5,6,7,8
-     *                                    lhs_info.k0: 2,3,4,8,16
-     *                                    lhs_info.v0: greater than 0
-     *                                    lhs_info.transpose: true, false
-     *                                    lhs_info.interleave: true, false
-     * @param[in] reinterpret_input_as_3d True if the input has to be reinterpreted as 3D tensor
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d);
-
-    // Inherited methods overridden
-    void run(const Window &window, cl::CommandQueue &queue) override;
-
-private:
-    const ICLTensor *_input;
-    ICLTensor       *_output;
-    bool             _reinterpret_input_as_3d;
-};
-} // namespace arm_compute
-#endif /* ARM_COMPUTE_CLGEMMRESHAPELHSMATRIXKERNEL_H */
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h b/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h
deleted file mode 100644
index 911484ea76..0000000000
--- a/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * Copyright (c) 2018-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLGEMMRESHAPERHSMATRIXKERNEL_H
-#define ARM_COMPUTE_CLGEMMRESHAPERHSMATRIXKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** OpenCL kernel to reshape the RHS matrix when performing the matrix multiplication
- *  In particular, this kernel splits the input matrix in blocks of size K0xN0 and stores each one in
- *  the output matrix unrolling the values */
-class CLGEMMReshapeRHSMatrixKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLGEMMReshapeRHSMatrixKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMReshapeRHSMatrixKernel(const CLGEMMReshapeRHSMatrixKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLGEMMReshapeRHSMatrixKernel &operator=(const CLGEMMReshapeRHSMatrixKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLGEMMReshapeRHSMatrixKernel(CLGEMMReshapeRHSMatrixKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLGEMMReshapeRHSMatrixKernel &operator=(CLGEMMReshapeRHSMatrixKernel &&) = default;
-    /** Default destructor */
-    ~CLGEMMReshapeRHSMatrixKernel() = default;
-    /** Initialise the kernel's input and output.
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will guarantee the OpenCL pitch alignment for the output tensor,
-     *       required to create a OpenCL image object from buffer in @ref CLGEMMMatrixMultiplyReshapedKernel and in @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-     *       Since the OpenCL image object is created importing the OpenCL buffer, the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32, F16
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# output width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# output (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *       -# The output tensor should be only consumed by @ref CLGEMMMatrixMultiplyReshapedKernel or @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-     *
-     * @param[in]  input    Input tensor. Data types supported: All
-     * @param[out] output   Output tensor. Data type supported: same as @p input
-     * @param[in]  rhs_info RHS matrix information to be used for reshaping. This object contains all the necessary
-     *                      information to reshape the input tensor. Only the following values are supported:
-     *                      rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image == true)
-     *                      rhs_info.k0: 1,2,3,4,8,16 (k0 = 1 only if rhs_info.transpose = false), (only 4, 8 and 16 if rhs_info.export_to_cl_image == true)
-     *                      rhs_info.h0: greater than 0
-     *                      rhs_info.transpose: true, false
-     *                      rhs_info.interleave: true, false
-     */
-    void configure(const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info);
-    /** Initialise the kernel's input and output.
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will guarantee the OpenCL pitch alignment for the output tensor,
-     *       required to create a OpenCL image object from buffer in @ref CLGEMMMatrixMultiplyReshapedKernel and in @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-     *       Since the OpenCL image object is created importing the OpenCL buffer, the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32, F16
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# output width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# output (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *       -# The output tensor should be only consumed by @ref CLGEMMMatrixMultiplyReshapedKernel or @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-     *
-     * @param[in]  compile_context The compile context to be used.
-     * @param[in]  input           Input tensor. Data types supported: All
-     * @param[out] output          Output tensor. Data type supported: same as @p input
-     * @param[in]  rhs_info        RHS matrix information to be used for reshaping. This object contains all the necessary
-     *                             information to reshape the input tensor. Only the following values are supported:
-     *                             rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image == true)
-     *                             rhs_info.k0: 1,2,3,4,8,16 (k0 = 1 only if rhs_info.transpose = false), (only 4, 8 and 16 if rhs_info.export_to_cl_image == true)
-     *                             rhs_info.h0: greater than 0
-     *                             rhs_info.transpose: true, false
-     *                             rhs_info.interleave: true, false
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMReshapeRHSMatrixKernel
-     *
-     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will guarantee the OpenCL pitch alignment for the output tensor,
-     *       required to create a OpenCL image object from buffer in @ref CLGEMMMatrixMultiplyReshapedKernel and in @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-     *       Since the OpenCL image object is created importing the OpenCL buffer, the following conditions are required:
-     *       -# rhs_info.n0 can only be 4, 8 and 16
-     *       -# rhs_info.k0 can only be 4, 8 and 16
-     *       -# Data type can only be F32, F16
-     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
-     *       -# output width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
-     *       -# output (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
-     *       -# The output tensor should be only consumed by @ref CLGEMMMatrixMultiplyReshapedKernel or @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-     *
-     * @param[in] input    Input tensor info. Data types supported: All
-     * @param[in] output   Output tensor info which stores the interleaved matrix. Data type supported: same as @p input.
-     * @param[in] rhs_info RHS matrix information to be used for reshaping. This object contains all the necessary
-     *                     information to reshape the input tensor. Only the following values are supported:
-     *                     rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image == true)
-     *                     rhs_info.k0: 1,2,3,4,8,16 (k0 = 1 only if rhs_info.transpose = false),(only 4, 8 and 16 if rhs_info.export_to_cl_image == true)
-     *                     rhs_info.h0: greater than 0
-     *                     rhs_info.transpose: true, false
-     *                     rhs_info.interleave: true, false
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const GEMMRHSMatrixInfo &rhs_info);
-
-    // Inherited methods overridden
-    void run(const Window &window, cl::CommandQueue &queue) override;
-
-private:
-    const ICLTensor *_input;
-    ICLTensor       *_output;
-};
-} // namespace arm_compute
-#endif /* ARM_COMPUTE_CLGEMMRESHAPERHSMATRIXKERNEL_H */
\ No newline at end of file
diff --git a/src/core/ITensorPack.cpp b/src/core/ITensorPack.cpp
index 546f669985..9eaeece271 100644
--- a/src/core/ITensorPack.cpp
+++ b/src/core/ITensorPack.cpp
@@ -27,14 +27,23 @@
 
 namespace arm_compute
 {
+ITensorPack::ITensorPack(std::initializer_list<PackElement> l)
+    : _pack()
+{
+    for(auto &e : l)
+    {
+        _pack[e.id] = e;
+    }
+}
+
 void ITensorPack::add_tensor(int id, ITensor *tensor)
 {
-    _pack[id] = PackElement(tensor);
+    _pack[id] = PackElement(id, tensor);
 }
 
 void ITensorPack::add_tensor(int id, const ITensor *tensor)
 {
-    _pack[id] = PackElement(tensor);
+    _pack[id] = PackElement(id, tensor);
 }
 
 void ITensorPack::add_const_tensor(int id, const ITensor *tensor)
diff --git a/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp b/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp
index 18d648d2f2..0a5101f564 100644
--- a/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp
@@ -35,7 +35,7 @@
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLUtils.h"
 #include "src/core/CL/CLValidate.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "support/Cast.h"
@@ -416,7 +416,7 @@ void ClDirectConvolutionKernel::configure(const CLCompileContext &compile_contex
 
         const unsigned int n0                 = win_config.second.x().step();
         const unsigned int m0                 = win_config.second.y().step();
-        const unsigned int k0                 = adjust_vec_size(is_data_type_quantized(data_type)? 16u : 8u, src->dimension(channel_idx));
+        const unsigned int k0                 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, src->dimension(channel_idx));
         const unsigned int partial_store_n0   = dst->dimension(channel_idx) % n0;
         const unsigned int pad_left           = conv_info.pad_left();
         const unsigned int pad_top            = conv_info.pad_top();
@@ -425,7 +425,7 @@ void ClDirectConvolutionKernel::configure(const CLCompileContext &compile_contex
         // Update the padding for the weights tensor if we can export to cl_image
         if(export_to_cl_image)
         {
-            arm_compute::cl_gemm::update_padding_for_cl_image(weights);
+            gemm::update_padding_for_cl_image(weights);
         }
 
         if(biases != nullptr)
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp
index 479c06330d..817a105b14 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
@@ -36,54 +36,54 @@
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/utils/helpers/float_ops.h"
+#include "support/Cast.h"
 #include "support/StringSupport.h"
 
-#include <set>
-#include <string>
-
 namespace arm_compute
 {
-using namespace arm_compute::misc::shape_calculator;
-
+namespace opencl
+{
+namespace kernels
+{
 namespace
 {
 using ElementsProcessed = Steps;
 
-inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float beta,
+inline Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float beta,
                                  bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((fp_mixed_precision && (input0->data_type() != DataType::F16)), "Mixed precision floating point is supported only for F16 data");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((fp_mixed_precision && (src0->data_type() != DataType::F16)), "Mixed precision floating point is supported only for F16 data");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((reshape_info.reinterpret_input_as_3d() || reshape_info.depth_output_gemm3d() != 0) && (input2 != nullptr)
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The src1 tensor cannot have more than 2 dimensions if src0 has to be reinterpreted as 3D");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((reshape_info.reinterpret_input_as_3d() || reshape_info.depth_output_gemm3d() != 0) && (src2 != nullptr)
                                     && (!reshape_info.broadcast_bias()),
-                                    "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D");
+                                    "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
 
     if(!is_interleaved_transposed)
     {
-        ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+        ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != src1->dimension(1));
 
-        if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+        if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
         {
-            const unsigned int m           = reshape_info.reinterpret_input_as_3d() ? input0->dimension(1) * input0->dimension(2) : input0->dimension(1);
-            const unsigned int n           = input1->dimension(0);
-            const unsigned int input2_dim0 = input2->dimension(0);
-            const unsigned int input2_dim1 = input2->dimension(1);
+            const unsigned int m         = reshape_info.reinterpret_input_as_3d() ? src0->dimension(1) * src0->dimension(2) : src0->dimension(1);
+            const unsigned int n         = src1->dimension(0);
+            const unsigned int src2_dim0 = src2->dimension(0);
+            const unsigned int src2_dim1 = src2->dimension(1);
 
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
             if(reshape_info.broadcast_bias())
             {
-                ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
             }
             else
             {
-                ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
             }
         }
     }
@@ -96,7 +96,7 @@ inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *i
         const int         k                         = reshape_info.k();
         const int         mult_transpose1xW_width   = reshape_info.mult_transpose1xW_width();
         const int         mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
-        rhs_info.n0                                 = max_cl_vector_width / input1->element_size();
+        rhs_info.n0                                 = max_cl_vector_width / src1->element_size();
         rhs_info.k0                                 = 1;
         rhs_info.h0                                 = mult_transpose1xW_width;
         rhs_info.interleave                         = false;
@@ -107,51 +107,51 @@ inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *i
         lhs_info.interleave                         = true;
         lhs_info.transpose                          = true;
 
-        TensorShape tensor_shape0{ input0->tensor_shape() };
+        TensorShape tensor_shape0{ src0->tensor_shape() };
         tensor_shape0.set(0, k);
         tensor_shape0.set(1, m);
 
-        TensorShape tensor_shape1{ input1->tensor_shape() };
+        TensorShape tensor_shape1{ src1->tensor_shape() };
         tensor_shape1.set(0, n);
         tensor_shape1.set(1, k);
 
-        const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
-        const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
+        const TensorInfo tensor_info0 = src0->clone()->set_tensor_shape(tensor_shape0);
+        const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
 
-        const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_lhs_reshaped_shape(tensor_info0, lhs_info));
-        const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
+        const TensorInfo tensor_info_reshaped0 = src0->clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(tensor_info0, lhs_info));
+        const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info));
 
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src0, &tensor_info_reshaped0);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
 
-        if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+        if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
         {
-            const unsigned int input2_dim0 = input2->dimension(0);
-            const unsigned int input2_dim1 = input2->dimension(1);
+            const unsigned int src2_dim0 = src2->dimension(0);
+            const unsigned int src2_dim1 = src2->dimension(1);
 
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
             if(reshape_info.broadcast_bias())
             {
-                ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
             }
             else
             {
-                ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
             }
         }
     }
 
-    if(output->total_size() != 0)
+    if(dst->total_size() != 0)
     {
-        const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, is_interleaved_transposed, reshape_info));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
     }
 
     return Status{};
 }
 
-inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output,
+inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst,
                                                                float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target,
                                                                ElementsProcessed &num_elements_processed)
 {
@@ -160,13 +160,13 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu
     Window win{};
     Window win_out{};
 
-    const DataType data_type                           = input0->data_type();
+    const DataType data_type                           = src0->data_type();
     unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
     unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
     bool           reinterpret_input_as_3d             = reshape_info.reinterpret_input_as_3d();
     bool           reinterpret_output_as_3d            = (reshape_info.depth_output_gemm3d() != 0);
 
-    // In case both input and output have to be reinterpreted as 3D tensors,
+    // In case both input and dst have to be reinterpreted as 3D tensors,
     // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
     if(reinterpret_input_as_3d == reinterpret_output_as_3d)
     {
@@ -174,16 +174,16 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu
         reinterpret_output_as_3d = false;
     }
 
-    // Output tensor auto inizialitation if not yet initialized
-    auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info)));
+    // dst tensor auto inizialitation if not yet initialized
+    auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, is_interleaved_transposed, reshape_info)));
 
-    TensorInfo tmp_info(*output);
+    TensorInfo tmp_info(*dst);
 
     if(reinterpret_output_as_3d)
     {
-        // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
+        // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
         // the window needs to be constructed on the 2D collapsed version of the tensor
-        TensorShape tmp_shape(output->tensor_shape());
+        TensorShape tmp_shape(dst->tensor_shape());
         tmp_shape.collapse(2U, 1U);
         tmp_info.set_tensor_shape(tmp_shape);
     }
@@ -198,63 +198,63 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu
         num_elems_processed_per_iteration_y = 4;
 
         win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-        if(input2 != nullptr)
+        if(src2 != nullptr)
         {
             const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
 
             const int bias_processed_per_iteration_y = reshape_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y;
 
-            AccessWindowStatic input2_access(input2, 0, 0,
-                                             ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x),
-                                             ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y));
+            AccessWindowStatic src2_access(src2, 0, 0,
+                                           ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x),
+                                           ceil_to_multiple(src2->dimension(1), bias_processed_per_iteration_y));
 
-            window_changed = update_window_and_padding(win, input2_access); // window used by the execute_window_loop
+            window_changed = update_window_and_padding(win, src2_access); // window used by the execute_window_loop
         }
     }
     else // The input tensors have not been reshaped
     {
-        // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x is set up for the default case.
+        // Special case for 1xN, 2xN, 3xN and 4xN src0 tensor. num_elems_processed_per_iteration_x is set up for the default case.
         num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
-        num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
+        num_elems_processed_per_iteration_y = std::min(static_cast<int>(dst->dimension(1)), 4);
 
         // Create kernels according to the architecture, data type and input size.
         GPUTarget arch_target = get_arch_from_target(gpu_target);
         if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32)
         {
-            num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000 && input0->num_dimensions() == 1) ? 2 : 4;
+            num_elems_processed_per_iteration_x = (src1->dimension(0) <= 1000 && src0->num_dimensions() == 1) ? 2 : 4;
         }
 
         // Configure window
         win     = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-        win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-        AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1));
-        AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
-        AccessWindowStatic output_access(output, 0, 0,
-                                         output->dimension(0),
-                                         output->dimension(1));
-
-        if(input2 != nullptr)
+        win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+        AccessWindowStatic src0_access(src0, 0, 0, src0->dimension(0), src0->dimension(1));
+        AccessWindowStatic src1_access(src1, 0, 0, ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x), src1->dimension(1));
+        AccessWindowStatic dst_access(dst, 0, 0,
+                                      dst->dimension(0),
+                                      dst->dimension(1));
+
+        if(src2 != nullptr)
         {
             const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
 
-            AccessWindowStatic input2_access(input2, 0, 0,
-                                             ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x),
-                                             input2->dimension(1));
+            AccessWindowStatic src2_access(src2, 0, 0,
+                                           ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x),
+                                           src2->dimension(1));
 
-            window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop
-                             update_window_and_padding(win_out, output_access);                             // window used to update the padding requirements of output tensor
+            window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop
+                             update_window_and_padding(win_out, dst_access);                          // window used to update the padding requirements of dst tensor
         }
         else
         {
-            window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
-                             update_window_and_padding(win_out, output_access);              // window used to update the padding requirements of output tensor
+            window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop
+                             update_window_and_padding(win_out, dst_access);             // window used to update the padding requirements of dst tensor
         }
     }
 
     // Collapse along the Z direction
     // This collapse needs to be here in order to tune the Z dimension of LWS
     Window             collapsed             = win;
-    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
+    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
     collapsed                                = win.collapse(win, dimension_to_collapse);
 
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
@@ -262,40 +262,23 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu
 }
 } // namespace
 
-CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel()
-    : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _add_bias(false),
-      _broadcast_bias(false)
-{
-}
-
-void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
-                                           bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision, activation_info);
-}
-
-void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
+void ClGemmMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha,
                                            float beta,
                                            bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
 {
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
 
     // Perform validate step
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr) ? input2->info() : nullptr, output->info(), beta,
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, beta,
                                                   is_interleaved_transposed, reshape_info, fp_mixed_precision));
 
-    auto padding_info = is_interleaved_transposed ? get_padding_info({ input0, input1, output }) : get_padding_info({ input0, output });
+    auto padding_info = is_interleaved_transposed ? get_padding_info({ src0, src1, dst }) : get_padding_info({ src0, dst });
 
-    _input0                   = input0;
-    _input1                   = input1;
-    _input2                   = helpers::float_ops::is_zero(beta) ? nullptr : input2;
-    _output                   = output;
     _reinterpret_input_as_3d  = reshape_info.reinterpret_input_as_3d();
     _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
-    _add_bias                 = _input2 != nullptr;
-    _broadcast_bias           = reshape_info.broadcast_bias();
+    _add_bias                 = src2 != nullptr;
 
-    // In case both input and output have to be reinterpreted as 3D tensors,
+    // In case both input and dst have to be reinterpreted as 3D tensors,
     // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
     if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
     {
@@ -304,11 +287,11 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte
     }
 
     // Check if we need to slide the matrix B
-    const unsigned int num_dimensions_input0 = _reinterpret_input_as_3d ? _input0->info()->num_dimensions() - 1 : _input0->info()->num_dimensions();
+    const unsigned int num_dimensions_src0 = _reinterpret_input_as_3d ? src0->num_dimensions() - 1 : src0->num_dimensions();
 
-    _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0);
+    _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0);
 
-    const DataType data_type = input0->info()->data_type();
+    const DataType data_type = src0->data_type();
 
     // Get target architecture
     GPUTarget gpu_target = get_target();
@@ -316,19 +299,19 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte
     ElementsProcessed num_elements_processed{};
 
     // Configure kernel window
-    auto win_config = validate_and_configure_window(input0->info(), input1->info(), (input2 != nullptr) ? input2->info() : nullptr, output->info(), beta, is_interleaved_transposed, reshape_info,
+    auto win_config = validate_and_configure_window(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info,
                                                     gpu_target, num_elements_processed);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
 
     // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true, both will be turned off (false)
     // in which case we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel.
-    // This means that the actual m used by the kernel is given by output->info()->dimension(1)
-    const unsigned int internal_m = _reinterpret_output_as_3d ? output->info()->dimension(1) * output->info()->dimension(2) : output->info()->dimension(1);
-    const unsigned int n          = output->info()->dimension(0);
+    // This means that the actual m used by the kernel is given by dst->dimension(1)
+    const unsigned int internal_m = _reinterpret_output_as_3d ? dst->dimension(1) * dst->dimension(2) : dst->dimension(1);
+    const unsigned int n          = dst->dimension(0);
 
-    const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(1) : input0->info()->dimension(1);
-    const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(2) : input0->info()->dimension(2);
+    const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1);
+    const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2);
 
     const unsigned int m0 = num_elements_processed.y();
     const unsigned int n0 = num_elements_processed.x();
@@ -341,18 +324,18 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte
     CLBuildOptions build_opts;
 
     build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
-    build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
+    build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
     build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
     build_opts.add_option_if(reshape_info.broadcast_bias(), "-DBROADCAST_BIAS");
     build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
     build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
     build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d));
     build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d));
-    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
+    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
     build_opts.add_option_if(activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(activation_info.activation())));
     build_opts.add_option_if(activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(activation_info.a()));
     build_opts.add_option_if(activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(activation_info.b()));
-    build_opts.add_option("-DIN1_DIM_X=" + support::cpp11::to_string(input1->info()->dimension(0)));
+    build_opts.add_option("-DIN1_DIM_X=" + support::cpp11::to_string(src1->dimension(0)));
 
     const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
 
@@ -364,7 +347,7 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte
 
         build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m));
         build_opts.add_option("-DN=" + support::cpp11::to_string(n));
-        build_opts.add_option("-DK=" + support::cpp11::to_string(input1->info()->dimension(0) / (n0 * mult_transpose1xW_width)));
+        build_opts.add_option("-DK=" + support::cpp11::to_string(src1->dimension(0) / (n0 * mult_transpose1xW_width)));
         build_opts.add_option("-DH0=" + support::cpp11::to_string(mult_transpose1xW_width));
         build_opts.add_option("-DV0=" + support::cpp11::to_string(mult_interleave4x4_height));
         build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
@@ -387,7 +370,7 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte
     else // The input tensors have not been reshaped
     {
         build_opts.add_option("-DN=" + support::cpp11::to_string(n));
-        build_opts.add_option("-DK=" + support::cpp11::to_string(input0->info()->dimension(0)));
+        build_opts.add_option("-DK=" + support::cpp11::to_string(src0->dimension(0)));
         build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
         build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
         build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
@@ -399,7 +382,7 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte
         {
             kernel_name = "gemm_mm_floating_point";
 
-            if(input0->info()->num_dimensions() != 1)
+            if(src0->num_dimensions() != 1)
             {
                 kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
                 if(fp_mixed_precision && data_type == DataType::F16)
@@ -408,10 +391,10 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte
                     kernel_name += "_acc32";
                 }
             }
-            else if(input1->info()->dimension(0) <= 1000 && data_type == DataType::F32)
+            else if(src1->dimension(0) <= 1000 && data_type == DataType::F32)
             {
-                // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and
-                // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g.
+                // The first kernel is optimized for the case of 1000 or less dst elements (e.g. FC8 of AlexNet and VGG-16, and
+                // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 dst elements (e.g.
                 // FC6 and FC7 of AlexNet and VGG-16).
                 kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000";
             }
@@ -432,37 +415,37 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte
     _config_id = "gemm_";
     _config_id += (is_interleaved_transposed ? "reshaped_" : "");
     _config_id += (_add_bias ? "add_bias_" : "");
-    _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
+    _config_id += (reshape_info.broadcast_bias() ? "broadcast_bias_" : "");
     _config_id += (fp_mixed_precision ? "fp_mixed_" : "");
     _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
     _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
-    _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
+    _config_id += lower_string(string_from_data_type(src0->data_type()));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(1));
+    _config_id += support::cpp11::to_string(dst->dimension(1));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(0));
+    _config_id += support::cpp11::to_string(dst->dimension(0));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(2));
+    _config_id += support::cpp11::to_string(dst->dimension(2));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(3));
+    _config_id += support::cpp11::to_string(dst->dimension(3));
     _config_id += "_";
-    _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1)));
+    _config_id += (is_interleaved_transposed ? support::cpp11::to_string(src1->dimension(0)) : support::cpp11::to_string(src1->dimension(1)));
 
     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }
 
-Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
+Status ClGemmMatrixMultiplyKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
                                             bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
 {
     // Note: num_elements_processed will be set in validate_and_configure_window()
     ElementsProcessed num_elements_processed{};
     ARM_COMPUTE_UNUSED(alpha);
     ARM_COMPUTE_UNUSED(activation_info);
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
-                                                              input1->clone().get(),
-                                                              (input2 != nullptr) ? input2->clone().get() : nullptr,
-                                                              output->clone().get(),
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(),
+                                                              src1->clone().get(),
+                                                              (src2 != nullptr) ? src2->clone().get() : nullptr,
+                                                              dst->clone().get(),
                                                               beta,
                                                               is_interleaved_transposed,
                                                               reshape_info,
@@ -473,15 +456,23 @@ Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITe
     return Status{};
 }
 
-void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue)
+void ClGemmMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
-    if(_input1->info()->num_dimensions() < 3)
+    const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+    const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+    const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
+    auto       dst  = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
+    ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr);
+
+    if(src1->info()->num_dimensions() < 3)
     {
         // The stride_z for matrix B must be zero if we do not slice
-        ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);
+        ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
     }
 
     Window slice          = window.first_slice_window_3D();
@@ -496,15 +487,15 @@ void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &que
     {
         // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
         const unsigned int idx0                  = 3 * num_arguments_per_2D_tensor() + 3 + num_arguments_bias;
-        const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom;
+        const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom;
         _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
     }
 
     if(_reinterpret_output_as_3d)
     {
-        // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
+        // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor
         const unsigned int idx0                  = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0) + num_arguments_bias;
-        const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
+        const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom;
         _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
     }
 
@@ -519,22 +510,24 @@ void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &que
         }
 
         unsigned int idx = 0;
-        add_2D_tensor_argument(idx, _input0, slice);
-        add_2D_tensor_argument(idx, _input1, slice_b);
+        add_2D_tensor_argument(idx, src0, slice);
+        add_2D_tensor_argument(idx, src1, slice_b);
         if(_add_bias)
         {
-            add_2D_tensor_argument(idx, _input2, slice);
+            add_2D_tensor_argument(idx, src2, slice);
         }
-        add_2D_tensor_argument(idx, _output, slice);
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
+        add_2D_tensor_argument(idx, dst, slice);
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2]));
         if(_add_bias)
         {
-            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2]));
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2]));
         }
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2]));
         enqueue(queue, *this, slice, lws_hint());
     }
     while(window.slide_window_slice_3D(slice));
 }
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h
new file mode 100644
index 0000000000..c1601335ee
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2017-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_KERNEL_H
+#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/core/gpu/cl/IClKernel.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+/** OpenCL kernel to multiply two input matrices "A" and "B" and add a martix "C" if provided. All elements of the output matrix will be multiplied by alpha. In case matrix C is passed, it will be added to the previous result.
+ *  For the matrix C, the broadcast addition is supported if the flag "broadcast_bias" is set in the GEMMReshapeInfo object
+ *
+ * @note If the input tensors @p src0 and @p src1 have been reshaped respectively with @ref ClGemmReshapeLhsMatrixKernel" and @ref ClGemmReshapeRhsMatrixKernel,
+ *       the flag @p is_interleaved_transposed must be set to true
+ *
+ * @attention @p src1 tensor must have at least 2 dimensions (matrix)
+ */
+class ClGemmMatrixMultiplyKernel : public IClKernel
+{
+public:
+    ClGemmMatrixMultiplyKernel() = default;
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyKernel);
+    /** Initialise the kernel's input, output and alpha
+     *
+     * @param[in]  compile_context           The compile context to be used.
+     * @param[in]  src0                      Input tensor containing the Matrix A. Data types supported: F16/F32
+     * @param[in]  src1                      Input tensor containing the Matrix B. Data type supported: same as @p src0
+     * @param[in]  src2                      Input tensor containing the Matrix C (bias). Can be nullptr. Data type supported: same as @p src0
+     * @param[out] dst                       Output tensor to store the result of matrix multiplication. Data type supported: same as @p src0
+     * @param[in]  alpha                     Weight of the matrix product
+     * @param[in]  beta                      (Optional) Weight of vector C. Default value is 0. Only beta = 1 is currently supported.
+     * @param[in]  is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref ClGemmReshapeLhsMatrixKernel and @ref ClGemmReshapeRhsMatrixKernel
+     * @param[in]  reshape_info              (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
+     * @param[in]  fp_mixed_precision        (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy
+     * @param[in]  activation_info           (Optional) Activation to apply after the matrix multiplication
+     *
+     */
+    void configure(const ClCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta = 0.f,
+                   bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo());
+    /** Static function to check if given info will lead to a valid configuration
+     *
+     * Similar to @ref ClGemmMatrixMultiplyKernel::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
+                           bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo());
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+
+public:
+    bool _slide_matrix_b{ true };
+    bool _reinterpret_input_as_3d{ false };
+    bool _reinterpret_output_as_3d{ false };
+    bool _add_bias{ false };
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_KERNEL_H */
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
index 1fe298c0a1..97d64c433c 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
@@ -36,37 +36,36 @@
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/utils/helpers/float_ops.h"
+#include "support/Cast.h"
 #include "support/StringSupport.h"
 
-#include <cstddef>
-#include <cstdint>
-#include <tuple>
-
-using namespace arm_compute::misc::shape_calculator;
-
 namespace arm_compute
 {
+namespace opencl
+{
+namespace kernels
+{
 namespace
 {
 using ElementsProcessed = Steps;
 
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
+Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
                           const GEMMRHSMatrixInfo &rhs_info,
                           const GEMMKernelInfo    &gemm_info)
 {
     ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0");
     ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16);
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (input2 != nullptr)
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (src2 != nullptr)
                                     && (!gemm_info.broadcast_bias),
-                                    "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D");
+                                    "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for GEMM native");
 
@@ -78,45 +77,45 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1,
     ARM_COMPUTE_UNUSED(n);
     ARM_COMPUTE_UNUSED(k);
 
-    ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != k);
-    ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != n);
-    ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(1) != k);
+    ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != k);
+    ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(0) != n);
+    ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(1) != k);
     if(gemm_info.reinterpret_input_as_3d)
     {
-        ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != m);
+        ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != m);
     }
     else
     {
-        ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != m);
+        ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m);
     }
 
-    if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+    if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
     {
-        const unsigned int input2_dim0 = input2->dimension(0);
-        const unsigned int input2_dim1 = input2->dimension(1);
+        const unsigned int src2_dim0 = src2->dimension(0);
+        const unsigned int src2_dim1 = src2->dimension(1);
 
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
         if(gemm_info.broadcast_bias)
         {
-            ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
         }
         else
         {
-            ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
         }
     }
 
-    if(output->total_size() != 0)
+    if(dst->total_size() != 0)
     {
-        const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
     }
 
     return Status{};
 }
 
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info,
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info,
                                                         const GEMMRHSMatrixInfo &rhs_info,
                                                         const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed)
 {
@@ -129,23 +128,23 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
     Window win_out{};
     bool   window_changed = false;
 
-    // In case both input and output have to be reinterpreted as 3D tensors,
+    // In case both input and dst have to be reinterpreted as 3D tensors,
     // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
     if(reinterpret_input_as_3d == reinterpret_output_as_3d)
     {
         reinterpret_output_as_3d = false;
     }
 
-    // Output tensor auto initialization if not yet initialized
-    auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)));
+    // dst tensor auto initialization if not yet initialized
+    auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
 
-    TensorInfo tmp_info(*output);
+    TensorInfo tmp_info(*dst);
 
     if(reinterpret_output_as_3d)
     {
-        // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
+        // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
         // the window needs to be constructed on the 2D collapsed version of the tensor
-        TensorShape tmp_shape(output->tensor_shape());
+        TensorShape tmp_shape(dst->tensor_shape());
         tmp_shape.collapse(2U, 1U);
         tmp_info.set_tensor_shape(tmp_shape);
     }
@@ -155,39 +154,39 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
     num_elems_processed_per_iteration_y = lhs_info.m0;
 
     win     = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-    win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
-    AccessWindowStatic input0_access(input0, 0, 0,
-                                     input0->dimension(0),
-                                     input0->dimension(1));
-    AccessWindowStatic input1_access(input1, 0, 0,
-                                     ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x),
-                                     input1->dimension(1));
-    AccessWindowStatic output_access(output, 0, 0,
-                                     output->dimension(0),
-                                     output->dimension(1));
-
-    if(input2 != nullptr)
+    win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+    AccessWindowStatic src0_access(src0, 0, 0,
+                                   src0->dimension(0),
+                                   src0->dimension(1));
+    AccessWindowStatic src1_access(src1, 0, 0,
+                                   ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x),
+                                   src1->dimension(1));
+    AccessWindowStatic dst_access(dst, 0, 0,
+                                  dst->dimension(0),
+                                  dst->dimension(1));
+
+    if(src2 != nullptr)
     {
         const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
 
-        AccessWindowStatic input2_access(input2, 0, 0,
-                                         ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x),
-                                         input2->dimension(1));
+        AccessWindowStatic src2_access(src2, 0, 0,
+                                       ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x),
+                                       src2->dimension(1));
 
-        window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop
-                         update_window_and_padding(win_out, output_access);                             // window used to update the padding requirements of output tensor
+        window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop
+                         update_window_and_padding(win_out, dst_access);                          // window used to update the padding requirements of dst tensor
     }
     else
     {
-        window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
-                         update_window_and_padding(win_out, output_access);              // window used to update the padding requirements of output tensor
+        window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop
+                         update_window_and_padding(win_out, dst_access);             // window used to update the padding requirements of dst tensor
     }
 
     // Collapse along the Z direction
     // This collapse needs to be here in order to tune the Z dimension of LWS
     Window             collapsed             = win;
-    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
+    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
     collapsed                                = win.collapse(win, dimension_to_collapse);
 
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
@@ -195,40 +194,22 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
 }
 } // namespace
 
-CLGEMMMatrixMultiplyNativeKernel::CLGEMMMatrixMultiplyNativeKernel()
-    : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false),
-      _add_bias(false), _broadcast_bias(false)
-{
-}
-
-void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
-                                                 const GEMMLHSMatrixInfo &lhs_info,
-                                                 const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info);
-}
-
-void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
+void ClGemmMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha,
                                                  float                    beta,
                                                  const GEMMLHSMatrixInfo &lhs_info,
                                                  const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
 {
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
 
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
 
-    auto padding_info         = get_padding_info({ input0, output });
-    _input0                   = input0;
-    _input1                   = input1;
-    _input2                   = helpers::float_ops::is_zero(beta) ? nullptr : input2;
-    _output                   = output;
+    auto padding_info         = get_padding_info({ src0, dst });
     _reinterpret_input_as_3d  = gemm_info.reinterpret_input_as_3d;
     _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
     _use_dummy_work_items     = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
-    _add_bias                 = _input2 != nullptr;
-    _broadcast_bias           = gemm_info.broadcast_bias;
+    _add_bias                 = src2 != nullptr;
 
-    // In case both input and output have to be reinterpreted as 3D tensors,
+    // In case both input and dst have to be reinterpreted as 3D tensors,
     // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
     if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
     {
@@ -237,23 +218,23 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile
     }
 
     // Check if we need to slide the matrix B
-    const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();
-    _slide_matrix_b                          = (_input1->info()->num_dimensions() >= num_dimensions_input0);
+    const unsigned int num_dimensions_src0 = src0->num_dimensions();
+    _slide_matrix_b                        = (src1->num_dimensions() >= num_dimensions_src0);
 
     ElementsProcessed num_elements_processed{};
 
     // Configure kernel window
-    auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
+    auto win_config = validate_and_configure_window(src0, src1, src2 != nullptr ? src2 : nullptr, dst, lhs_info, rhs_info, gemm_info, num_elements_processed);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-    ICLKernel::configure_internal(win_config.second);
+    IClKernel::configure_internal(win_config.second);
 
     // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true,
     // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel.
-    // This means that the actual m used by the kernel is given by output->info()->dimension(1) and not by gemm_info.m
-    const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1);
+    // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m
+    const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1);
 
-    const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(1) : input0->info()->dimension(1);
-    const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(2) : input0->info()->dimension(2);
+    const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1);
+    const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2);
 
     // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
     const unsigned int partial_store_m0 = internal_m % lhs_info.m0;
@@ -265,16 +246,16 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile
 
     // Create build options
     CLBuildOptions build_opts;
-    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type()));
     build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
-    build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
+    build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
     build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
     build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS");
     build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
     build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
     build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d));
     build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d));
-    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
+    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
     build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
     build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m));
     build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n));
@@ -297,19 +278,19 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile
     _config_id = kernel_name;
     _config_id += "_";
     _config_id += (_add_bias ? "add_bias_" : "");
-    _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
+    _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : "");
     _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
     _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
     _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : "");
-    _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
+    _config_id += lower_string(string_from_data_type(src0->data_type()));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(1));
+    _config_id += support::cpp11::to_string(dst->dimension(1));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(0));
+    _config_id += support::cpp11::to_string(dst->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(gemm_info.k);
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(2));
+    _config_id += support::cpp11::to_string(dst->dimension(2));
     _config_id += "_";
     _config_id += support::cpp11::to_string(lhs_info.m0);
     _config_id += "_";
@@ -320,16 +301,16 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile
     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }
 
-Status CLGEMMMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
+Status ClGemmMatrixMultiplyNativeKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
                                                   const GEMMLHSMatrixInfo &lhs_info,
                                                   const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
 {
     ElementsProcessed num_elements_processed{};
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
-                                                              input1->clone().get(),
-                                                              input2 != nullptr ? input2->clone().get() : nullptr,
-                                                              output->clone().get(),
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(),
+                                                              src1->clone().get(),
+                                                              src2 != nullptr ? src2->clone().get() : nullptr,
+                                                              dst->clone().get(),
                                                               lhs_info,
                                                               rhs_info,
                                                               gemm_info,
@@ -339,15 +320,23 @@ Status CLGEMMMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, con
     return Status{};
 }
 
-void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueue &queue)
+void ClGemmMatrixMultiplyNativeKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
-    if(_input1->info()->num_dimensions() < 3)
+    const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+    const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+    const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
+    auto       dst  = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
+    ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr);
+
+    if(src1->info()->num_dimensions() < 3)
     {
         // The stride_z for matrix B must be zero if we do not slice
-        ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);
+        ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
     }
 
     Window slice          = window.first_slice_window_3D();
@@ -368,13 +357,13 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu
         {
             idx0 = 3 * num_arguments_per_2D_tensor() + 3;
         }
-        const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom;
+        const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom;
         _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
     }
 
     if(_reinterpret_output_as_3d)
     {
-        // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
+        // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor
         unsigned int idx0;
         if(_add_bias)
         {
@@ -384,7 +373,7 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu
         {
             idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
         }
-        const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
+        const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom;
         _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
     }
 
@@ -399,22 +388,24 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu
         }
 
         unsigned int idx = 0;
-        add_2D_tensor_argument(idx, _input0, slice);
-        add_2D_tensor_argument(idx, _input1, slice_b);
+        add_2D_tensor_argument(idx, src0, slice);
+        add_2D_tensor_argument(idx, src1, slice_b);
         if(_add_bias)
         {
-            add_2D_tensor_argument(idx, _input2, slice);
+            add_2D_tensor_argument(idx, src2, slice);
         }
-        add_2D_tensor_argument(idx, _output, slice);
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
+        add_2D_tensor_argument(idx, dst, slice);
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2]));
         if(_add_bias)
         {
-            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2]));
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2]));
         }
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2]));
         enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
     }
     while(window.slide_window_slice_3D(slice));
 }
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h
new file mode 100644
index 0000000000..4770b18b8e
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2019-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_NATIVE_KERNEL_H
+#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_NATIVE_KERNEL_H
+
+#include "arm_compute/core/KernelDescriptors.h"
+#include "src/core/common/Macros.h"
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/core/gpu/cl/IClKernel.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+/** OpenCL kernel to multiply matrices when neither of the input matrices have been reshaped */
+class ClGemmMatrixMultiplyNativeKernel : public IClKernel
+{
+public:
+    ClGemmMatrixMultiplyNativeKernel() = default;
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyNativeKernel);
+    /** Initialise the kernel's input and dst.
+     *
+     * @param[in]  compile_context The compile context to be used.
+     * @param[in]  src0            Input tensor for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4.
+     * @param[in]  src1            Input tensor for the RHS matrix. Data type supported: same as @p src0. The number of dimensions for the RHS matrix must be less or equal than 3.
+     * @param[in]  src2            Input tensor containing the bias matrix. Data type supported: same as @p src0.
+     * @param[out] dst             dst tensor info. Data type supported: same as @p src0
+     * @param[in]  alpha           Weight of the matrix product
+     * @param[in]  beta            Weight of the matrix bias
+     * @param[in]  lhs_info        LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported:
+     *                             lhs_info.m0: 1,2,3,4,5,6,7,8
+     *                             lhs_info.k0: 2,3,4,8,16
+     * @param[in]  rhs_info        RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported:
+     *                             rhs_info.n0: 2,3,4,8,16
+     *                             rhs_info.k0: same of lhs_info.k0
+     * @param[in]  gemm_info       GEMM information used to retrieve the original dimensions of the input matrices
+     */
+    void configure(const ClCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta,
+                   const GEMMLHSMatrixInfo &lhs_info,
+                   const GEMMRHSMatrixInfo &rhs_info,
+                   const GEMMKernelInfo    &gemm_info);
+    /** Static function to check if given info will lead to a valid configuration
+     *
+     * Similar to @ref ClGemmMatrixMultiplyNativeKernel::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
+                           const GEMMRHSMatrixInfo &rhs_info,
+                           const GEMMKernelInfo    &gemm_info);
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+
+private:
+    bool _slide_matrix_b{ true };
+    bool _reinterpret_input_as_3d{ false };
+    bool _reinterpret_output_as_3d{ false };
+    bool _use_dummy_work_items{ false };
+    bool _add_bias{ false };
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_NATIVE_KERNEL_H*/
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.cpp
index d270f92615..27409b66ac 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
@@ -35,39 +35,38 @@
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLUtils.h"
 #include "src/core/CL/CLValidate.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/utils/helpers/float_ops.h"
+#include "support/Cast.h"
 #include "support/StringSupport.h"
 
 #include <cstddef>
 #include <cstdint>
 #include <tuple>
 
-using namespace arm_compute;
-using namespace arm_compute::misc::shape_calculator;
-
 namespace arm_compute
 {
-class Coordinates;
-} // namespace arm_compute
-
+namespace opencl
+{
+namespace kernels
+{
 namespace
 {
 using ElementsProcessed = Steps;
 
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
+Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
                           const GEMMRHSMatrixInfo &rhs_info,
                           const GEMMKernelInfo    &gemm_info)
 {
     ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 != rhs_info.k0);
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.transpose == rhs_info.transpose);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(((lhs_info.k0 & (lhs_info.k0 - 1)) && lhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0");
@@ -75,60 +74,60 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1,
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 2 || lhs_info.m0 > 8);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((lhs_info.transpose) && ((lhs_info.m0 & (lhs_info.m0 - 1)) && lhs_info.m0 != 3), "Only 2,3,4,8,16 are supported for m0");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((rhs_info.transpose) && ((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (input2 != nullptr)
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (src2 != nullptr)
                                     && (!gemm_info.broadcast_bias),
-                                    "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision && (input0->data_type() == DataType::F32), "Mixed precision only supported for F16 data type");
-    ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(*input1, rhs_info));
+                                    "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision && (src0->data_type() == DataType::F32), "Mixed precision only supported for F16 data type");
+    ARM_COMPUTE_RETURN_ON_ERROR(gemm::validate_image2d_support_on_rhs(*src1, rhs_info));
 
     const unsigned int m = gemm_info.m;
     const unsigned int n = gemm_info.n;
     const unsigned int k = gemm_info.k;
 
-    TensorShape tensor_shape0{ input0->tensor_shape() };
+    TensorShape tensor_shape0{ src0->tensor_shape() };
     tensor_shape0.set(0, k);
     tensor_shape0.set(1, m);
 
-    TensorShape tensor_shape1{ input1->tensor_shape() };
+    TensorShape tensor_shape1{ src1->tensor_shape() };
     tensor_shape1.set(0, n);
     tensor_shape1.set(1, k);
 
-    if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+    if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
     {
-        const unsigned int input2_dim0 = input2->dimension(0);
-        const unsigned int input2_dim1 = input2->dimension(1);
+        const unsigned int src2_dim0 = src2->dimension(0);
+        const unsigned int src2_dim1 = src2->dimension(1);
 
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
         if(gemm_info.broadcast_bias)
         {
-            ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
         }
         else
         {
-            ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
         }
     }
 
-    const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
-    const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
+    const TensorInfo tensor_info0 = src0->clone()->set_tensor_shape(tensor_shape0);
+    const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
 
-    const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_lhs_reshaped_shape(tensor_info0, lhs_info));
-    const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
+    const TensorInfo tensor_info_reshaped0 = src0->clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(tensor_info0, lhs_info));
+    const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info));
 
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src0, &tensor_info_reshaped0);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
 
-    if(output->total_size() != 0)
+    if(dst->total_size() != 0)
     {
-        const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
     }
 
     return Status{};
 }
 
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info,
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info,
                                                         const GEMMRHSMatrixInfo &rhs_info,
                                                         const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed)
 {
@@ -140,16 +139,16 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
     Window win_out{};
     bool   window_changed = false;
 
-    // Output tensor auto initialization if not yet initialized
-    auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)));
+    // dst tensor auto initialization if not yet initialized
+    auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
 
-    TensorInfo tmp_info(*output);
+    TensorInfo tmp_info(*dst);
 
     if(reinterpret_output_as_3d)
     {
-        // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
+        // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
         // the window needs to be constructed on the 2D collapsed version of the tensor
-        TensorShape tmp_shape(output->tensor_shape());
+        TensorShape tmp_shape(dst->tensor_shape());
         tmp_shape.collapse(2U, 1U);
         tmp_info.set_tensor_shape(tmp_shape);
     }
@@ -159,25 +158,25 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
     num_elems_processed_per_iteration_y = lhs_info.m0;
 
     win     = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-    win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+    win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
 
-    if(input2 != nullptr)
+    if(src2 != nullptr)
     {
         const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
 
         const int bias_processed_per_iteration_y = gemm_info.broadcast_bias ? 1 : num_elems_processed_per_iteration_y;
 
-        AccessWindowStatic input2_access(input2, 0, 0,
-                                         ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x),
-                                         ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y));
+        AccessWindowStatic src2_access(src2, 0, 0,
+                                       ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x),
+                                       ceil_to_multiple(src2->dimension(1), bias_processed_per_iteration_y));
 
-        window_changed = update_window_and_padding(win, input2_access); // window used by the execute_window_loop
+        window_changed = update_window_and_padding(win, src2_access); // window used by the execute_window_loop
     }
 
     // Collapse along the Z direction
     // This collapse needs to be here in order to tune the Z dimension of LWS
     Window             collapsed             = win;
-    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
+    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
     collapsed                                = win.collapse(win, dimension_to_collapse);
 
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
@@ -185,56 +184,37 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
 }
 } // namespace
 
-CLGEMMMatrixMultiplyReshapedKernel::CLGEMMMatrixMultiplyReshapedKernel()
-    : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_output_as_3d(false), _use_dummy_work_items(false), _add_bias(false),
-      _broadcast_bias(false), _export_to_cl_image(false), _k(1)
-{
-}
-
-void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
-                                                   const GEMMLHSMatrixInfo &lhs_info,
-                                                   const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info);
-}
-
-void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
-                                                   float                    beta,
-                                                   const GEMMLHSMatrixInfo &lhs_info,
-                                                   const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
+void ClGemmMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compile_context,
+                                                   ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta,
+                                                   const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
 {
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
 
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
 
-    auto padding_info         = get_padding_info({ input0, output });
-    _input0                   = input0;
-    _input1                   = input1;
-    _input2                   = helpers::float_ops::is_zero(beta) ? nullptr : input2;
-    _output                   = output;
+    auto padding_info         = get_padding_info({ src0, dst });
     _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
     _use_dummy_work_items     = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
-    _add_bias                 = _input2 != nullptr;
-    _broadcast_bias           = gemm_info.broadcast_bias;
+    _add_bias                 = src2 != nullptr;
     _export_to_cl_image       = rhs_info.export_to_cl_image;
     _k                        = gemm_info.k;
 
     // Check if we need to slide the matrix B
-    const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();
-    _slide_matrix_b                          = (_input1->info()->num_dimensions() >= num_dimensions_input0);
+    const unsigned int num_dimensions_src0 = src0->num_dimensions();
+    _slide_matrix_b                        = (src1->num_dimensions() >= num_dimensions_src0);
 
     ElementsProcessed num_elements_processed{};
 
     // Configure kernel window
-    auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
+    auto win_config = validate_and_configure_window(src0, src1, src2, dst, lhs_info, rhs_info, gemm_info, num_elements_processed);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
 
     const bool     enable_mixed_precision = gemm_info.fp_mixed_precision;
-    const DataType data_type              = input0->info()->data_type();
+    const DataType data_type              = src0->data_type();
 
     // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
-    const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1);
+    const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1);
 
     const unsigned int partial_store_m0 = internal_m % lhs_info.m0;
     const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
@@ -242,13 +222,13 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compi
     // Create build options
     CLBuildOptions build_opts;
     build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
-    build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
+    build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
     build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
     build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
-    build_opts.add_option_if(_reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
-    build_opts.add_option_if(_reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
+    build_opts.add_option_if(_reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(dst->dimension(1)));
+    build_opts.add_option_if(_reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(dst->dimension(2)));
     build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS");
-    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
+    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
     build_opts.add_option_if(lhs_info.interleave, "-DLHS_INTERLEAVE");
     build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
     build_opts.add_option_if(lhs_info.transpose, "-DLHS_TRANSPOSE");
@@ -258,7 +238,7 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compi
     build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
     build_opts.add_option_if(enable_mixed_precision, "-DMIXED_PRECISION");
     build_opts.add_option_if(rhs_info.export_to_cl_image, "-DOPENCL_IMAGE_SUPPORT");
-    build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(input1->info()->dimension(1)));
+    build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(src1->dimension(1)));
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
     build_opts.add_option("-DDATA_TYPE_ACCUMULATOR=" + (enable_mixed_precision ? get_cl_type_from_data_type(DataType::F32) : get_cl_type_from_data_type(data_type)));
     build_opts.add_option("-DM=" + support::cpp11::to_string(gemm_info.m));
@@ -284,19 +264,19 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compi
     _config_id = kernel_name;
     _config_id += "_";
     _config_id += (_add_bias ? "add_bias_" : "");
-    _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
+    _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : "");
     _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
     _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : "");
-    _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
+    _config_id += lower_string(string_from_data_type(src0->data_type()));
     _config_id += "_";
     _config_id += (enable_mixed_precision ? "mixed_precision_" : "");
-    _config_id += support::cpp11::to_string(output->info()->dimension(1));
+    _config_id += support::cpp11::to_string(dst->dimension(1));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(0));
+    _config_id += support::cpp11::to_string(dst->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(gemm_info.k);
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(2));
+    _config_id += support::cpp11::to_string(dst->dimension(2));
     _config_id += "_";
     _config_id += support::cpp11::to_string(lhs_info.m0);
     _config_id += "_";
@@ -315,16 +295,16 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compi
     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }
 
-Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
+Status ClGemmMatrixMultiplyReshapedKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
                                                     const GEMMLHSMatrixInfo &lhs_info,
                                                     const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
 {
     ElementsProcessed num_elements_processed{};
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
-                                                              input1->clone().get(),
-                                                              input2 != nullptr ? input2->clone().get() : nullptr,
-                                                              output->clone().get(),
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(),
+                                                              src1->clone().get(),
+                                                              src2 != nullptr ? src2->clone().get() : nullptr,
+                                                              dst->clone().get(),
                                                               lhs_info,
                                                               rhs_info,
                                                               gemm_info,
@@ -334,15 +314,23 @@ Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, c
     return Status{};
 }
 
-void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQueue &queue)
+void ClGemmMatrixMultiplyReshapedKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
-    if(_input1->info()->num_dimensions() < 3)
+    const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+    const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+    const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
+    auto       dst  = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
+    ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr);
+
+    if(src1->info()->num_dimensions() < 3)
     {
         // The stride_z for matrix B must be zero if we do not slice
-        ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);
+        ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
     }
 
     Window slice          = window.first_slice_window_3D();
@@ -351,16 +339,16 @@ void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQu
     slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
     slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
 
-    const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
+    const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom;
 
-    cl::Image2D input1_image2d;
+    cl::Image2D src1_image2d;
 
     if(_export_to_cl_image)
     {
-        const TensorShape shape2d(_input1->info()->dimension(0) / 4, _input1->info()->dimension(1) * _input1->info()->dimension(2));
-        const size_t      image_row_pitch = _input1->info()->strides_in_bytes()[1];
+        const TensorShape shape2d(src1->info()->dimension(0) / 4, src1->info()->dimension(1) * src1->info()->dimension(2));
+        const size_t      image_row_pitch = src1->info()->strides_in_bytes()[1];
 
-        input1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input1->cl_buffer(), shape2d, _input1->info()->data_type(), image_row_pitch);
+        src1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), src1->cl_buffer(), shape2d, src1->info()->data_type(), image_row_pitch);
     }
 
     do
@@ -376,41 +364,41 @@ void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQu
         unsigned int idx = 0;
 
         // LHS buffer
-        add_2D_tensor_argument(idx, _input0, slice);
+        add_2D_tensor_argument(idx, src0, slice);
 
         // RHS buffer or RHS OpenCL image (_export_to_cl_image == true)
         if(_export_to_cl_image)
         {
-            _kernel.setArg(idx++, input1_image2d);
+            _kernel.setArg(idx++, src1_image2d);
         }
         else
         {
-            add_2D_tensor_argument(idx, _input1, slice_b);
+            add_2D_tensor_argument(idx, src1, slice_b);
         }
 
         // Bias buffer (_add_bias == true)
-        add_2D_tensor_argument_if(_add_bias, idx, _input2, slice);
+        add_2D_tensor_argument_if(_add_bias, idx, src2, slice);
 
-        // Output buffer
-        add_2D_tensor_argument(idx, _output, slice);
+        // dst buffer
+        add_2D_tensor_argument(idx, dst, slice);
 
         // K dimension (not used if _export_to_cl_image == true)
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_k));
 
         // LHS stride_z
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2]));
 
         // RHS stride_z (not used if _export_to_cl_image == true)
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2]));
 
         // Bias stride_z (if _add_bias == true)
         if(_add_bias)
         {
-            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2]));
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2]));
         }
 
-        // Output stride_z
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
+        // dst stride_z
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2]));
 
         // Cross-plan padding (if _reinterpret_output_as_3d = true)
         if(_reinterpret_output_as_3d)
@@ -423,3 +411,6 @@ void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQu
     }
     while(window.slide_window_slice_3D(slice));
 }
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h
new file mode 100644
index 0000000000..ab648f15ae
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2018-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_KERNEL_H
+#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/core/gpu/cl/IClKernel.h"
+
+#include "arm_compute/core/KernelDescriptors.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+/** OpenCL kernel to multiply matrices when both the input matrices LHS (src0) and RHS (src1) have been reshaped
+ *
+ * @note The input matrices @p src0 and @p src1 must be reshaped through:
+ *  - @ref ClGemmReshapeLhsMatrixKernel
+ *  - @ref ClGemmReshapeRhsMatrixKernel
+ */
+class ClGemmMatrixMultiplyReshapedKernel : public IClKernel
+{
+public:
+    ClGemmMatrixMultiplyReshapedKernel() = default;
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyReshapedKernel);
+    /** Initialise the kernel's input and output.
+     *
+     * @note The F16 computation also supports mixed precision through the gemm_info.fp_mixed_precision flag.
+     *       Mixed precision combines different floating precisions during the computation, in particular, F32 for the accumulations and F16 for the
+     *       multiplications. i.e. float c = (half)a * (half)b
+     *
+     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function.
+     *       Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer,
+     *       the following conditions are required:
+     *       -# rhs_info.n0 can only be 4, 8 and 16
+     *       -# rhs_info.k0 can only be 4, 8 and 16
+     *       -# Data type can only be F32
+     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
+     *       -# The stride Y for the src1 should satisfy the OpenCL pitch alignment requirement
+     *       -# src1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
+     *       -# src1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
+     *
+     * @param[in]  compile_context The compile context to be used.
+     * @param[in]  src0            Input tensor containing the LHS reshaped matrix. Data type supported: F16/F32  (only F32 if rhs_info.export_to_cl_image = true). The number of dimensions for the LHS matrix must be less or equal than 4
+     * @param[in]  src1            Input tensor containing the RHS reshaped matrix. Data type supported: same as @p src0. The number of dimensions for the RHS matrix must be less or equal than 3
+     * @param[in]  src2            Input tensor containing the bias matrix. Data type supported: same as @p src0.
+     * @param[out] dst             dst tensor to store the result of matrix multiplication. Data type supported: same as @p src0
+     * @param[in]  alpha           Weight of the matrix product
+     * @param[in]  beta            Weight of the matrix bias
+     * @param[in]  lhs_info        LHS matrix information used for reshaping the src0 tensor.  Only the following values are supported:
+     *                             lhs_info.m0: 2,3,4,5,6,7,8
+     *                             lhs_info.k0: 2,3,4,8,16
+     *                             lhs_info.transpose: false
+     * @param[in]  rhs_info        RHS matrix information used for reshaping the src1 tensor.  Only the following values are supported:
+     *                             rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true)
+     *                             rhs_info.k0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true)
+     *                             rhs_info.transpose: true
+     * @param[in]  gemm_info       GEMM information used to retrieve the original dimensions of the input matrices
+     *
+     * @note lhs_info.k0 must be equal to rhs_info.k0
+     */
+    void configure(const ClCompileContext &compile_context,
+                   ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta,
+                   const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info);
+    /** Static function to check if given info will lead to a valid configuration
+     *
+     * Similar to @ref ClGemmMatrixMultiplyReshapedKernel::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
+                           const GEMMRHSMatrixInfo &rhs_info,
+                           const GEMMKernelInfo    &gemm_info);
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+
+private:
+    bool         _slide_matrix_b{ true };
+    bool         _reinterpret_output_as_3d{ false };
+    bool         _use_dummy_work_items{ false };
+    bool         _add_bias{ false };
+    bool         _export_to_cl_image{ false };
+    unsigned int _k{ 1 };
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_KERNEL_H */
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp
index 3dee4f24cd..4eea2c6f76 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h"
 
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Helpers.h"
@@ -31,96 +31,95 @@
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLUtils.h"
 #include "src/core/CL/CLValidate.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/utils/helpers/float_ops.h"
+#include "support/Cast.h"
 #include "support/StringSupport.h"
 
-#include <tuple>
-
-using namespace arm_compute::misc::shape_calculator;
-
 namespace arm_compute
 {
+namespace opencl
+{
+namespace kernels
+{
 namespace
 {
 using ElementsProcessed = Steps;
 
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
-                          const GEMMRHSMatrixInfo &rhs_info,
-                          const GEMMKernelInfo    &gemm_info)
+Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
+                          const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
 {
     ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_info.m0 < 1 || lhs_info.m0 > 8, "Only 1,2,3,4,5,6,7,8 are supported for m0");
     ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16 || rhs_info.k0 < 2);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0");
     ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.n0 > 16 || rhs_info.n0 < 2);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (input2 != nullptr)
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (src2 != nullptr)
                                     && (!gemm_info.broadcast_bias),
-                                    "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D");
+                                    "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported");
-    ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(*input1, rhs_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(gemm::validate_image2d_support_on_rhs(*src1, rhs_info));
 
     const unsigned int m = gemm_info.m;
     const unsigned int n = gemm_info.n;
     const unsigned int k = gemm_info.k;
 
-    TensorShape tensor_shape1{ input1->tensor_shape() };
+    TensorShape tensor_shape1{ src1->tensor_shape() };
     tensor_shape1.set(0, n);
     tensor_shape1.set(1, k);
 
-    if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+    if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
     {
-        const unsigned int input2_dim0 = input2->dimension(0);
-        const unsigned int input2_dim1 = input2->dimension(1);
+        const unsigned int src2_dim0 = src2->dimension(0);
+        const unsigned int src2_dim1 = src2->dimension(1);
 
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input0);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src0);
         if(gemm_info.broadcast_bias)
         {
-            ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
         }
         else
         {
-            ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
         }
     }
 
-    const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
+    const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
 
-    const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
+    const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info));
 
-    ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != k);
+    ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != k);
     if(gemm_info.reinterpret_input_as_3d)
     {
-        ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != m);
+        ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != m);
     }
     else
     {
-        ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != m);
+        ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m);
     }
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
 
-    if(output->total_size() != 0)
+    if(dst->total_size() != 0)
     {
-        const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
     }
 
     return Status{};
 }
 
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info,
-                                                        const GEMMRHSMatrixInfo &rhs_info,
-                                                        const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info,
+                                                        const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed)
 {
     unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
     unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
@@ -131,24 +130,24 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
     Window win_out{};
     bool   window_changed = false;
 
-    // In case both input and output have to be reinterpreted as 3D tensors,
+    // In case both input and dst have to be reinterpreted as 3D tensors,
     // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
-    // This approach should only be used when the input/output tensors have pad on the y direction
+    // This approach should only be used when the input/dst tensors have pad on the y direction
     if((reinterpret_input_as_3d == reinterpret_output_as_3d) && gemm_info.has_pad_y)
     {
         reinterpret_output_as_3d = false;
     }
 
-    // Output tensor auto initialization if not yet initialized
-    auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)));
+    // dst tensor auto initialization if not yet initialized
+    auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
 
-    TensorInfo tmp_info(*output);
+    TensorInfo tmp_info(*dst);
 
     if(reinterpret_output_as_3d)
     {
-        // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
+        // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
         // the window needs to be constructed on the 2D collapsed version of the tensor
-        TensorShape tmp_shape(output->tensor_shape());
+        TensorShape tmp_shape(dst->tensor_shape());
         tmp_shape.collapse(2U, 1U);
         tmp_info.set_tensor_shape(tmp_shape);
     }
@@ -158,23 +157,23 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
     num_elems_processed_per_iteration_y = lhs_info.m0;
 
     win     = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-    win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+    win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
 
-    if(input2 != nullptr)
+    if(src2 != nullptr)
     {
         const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
 
-        AccessWindowStatic input2_access(input2, 0, 0,
-                                         ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x),
-                                         input2->dimension(1));
+        AccessWindowStatic src2_access(src2, 0, 0,
+                                       ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x),
+                                       src2->dimension(1));
 
-        window_changed = update_window_and_padding(win, input2_access);
+        window_changed = update_window_and_padding(win, src2_access);
     }
 
     // Collapse along the Z direction
     // This collapse needs to be here in order to tune the Z dimension of LWS
     Window             collapsed             = win;
-    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
+    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
     collapsed                                = win.collapse(win, dimension_to_collapse);
 
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
@@ -182,44 +181,24 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
 }
 } // namespace
 
-CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::CLGEMMMatrixMultiplyReshapedOnlyRHSKernel()
-    : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false),
-      _add_bias(false), _broadcast_bias(false), _export_to_cl_image(false), _has_pad_y(false)
-{
-}
-
-void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
-                                                          const GEMMLHSMatrixInfo &lhs_info,
-                                                          const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info);
-}
-
-void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output,
-                                                          float                    alpha,
-                                                          float                    beta,
-                                                          const GEMMLHSMatrixInfo &lhs_info,
-                                                          const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
+void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::configure(const CLCompileContext &compile_context,
+                                                          ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta,
+                                                          const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
 {
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
 
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
 
-    _input0                   = input0;
-    _input1                   = input1;
-    _input2                   = helpers::float_ops::is_zero(beta) ? nullptr : input2;
-    _output                   = output;
     _reinterpret_input_as_3d  = gemm_info.reinterpret_input_as_3d;
     _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
     _use_dummy_work_items     = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
-    _add_bias                 = _input2 != nullptr;
-    _broadcast_bias           = gemm_info.broadcast_bias;
+    _add_bias                 = src2 != nullptr;
     _export_to_cl_image       = rhs_info.export_to_cl_image;
     _has_pad_y                = gemm_info.has_pad_y;
 
-    auto padding_info = get_padding_info({ input0, input1, output });
+    auto padding_info = get_padding_info({ src0, src1, dst });
 
-    // In case both input and output have to be reinterpreted as 3D tensors,
+    // In case both input and dst have to be reinterpreted as 3D tensors,
     // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
     if((_reinterpret_input_as_3d == _reinterpret_output_as_3d) && _has_pad_y)
     {
@@ -228,24 +207,24 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext
     }
 
     // Check if we need to slide the matrix B
-    const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();
-    _slide_matrix_b                          = (_input1->info()->num_dimensions() >= num_dimensions_input0);
+    const unsigned int num_dimensions_src0 = src0->num_dimensions();
+    _slide_matrix_b                        = (src1->num_dimensions() >= num_dimensions_src0);
 
     ElementsProcessed num_elements_processed{};
 
     // Configure kernel window
-    auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
+    auto win_config = validate_and_configure_window(src0, src1, src2, dst, lhs_info, rhs_info, gemm_info, num_elements_processed);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
 
-    // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true,
+    // If _reinterpret_input_as_3d = reinterpret_output_as_3d = true,
     // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel.
-    // This means that the actual m used by the kernel is given by output->info()->dimension(1) and not by gemm_info.m
-    const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1);
+    // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m
+    const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1);
 
     // These variables are used only if gemm_info.has_pad_y == true
-    const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(1) : input0->info()->dimension(1);
-    const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(2) : input0->info()->dimension(2);
+    const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1);
+    const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2);
 
     // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads.
     // NOTE: This might have implications on heuristics and performance
@@ -257,16 +236,16 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext
 
     // Create build options
     CLBuildOptions build_opts;
-    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type()));
     build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
-    build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
+    build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
     build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
     build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS");
-    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
+    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
     build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
     build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
     build_opts.add_option_if(rhs_info.export_to_cl_image, "-DOPENCL_IMAGE_SUPPORT");
-    build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(input1->info()->dimension(1)));
+    build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(src1->dimension(1)));
     build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m));
     build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n));
     build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
@@ -299,19 +278,19 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext
     _config_id += "_";
     _config_id += (_has_pad_y ? "" : "no_pad_y_");
     _config_id += (_add_bias ? "add_bias_" : "");
-    _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
+    _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : "");
     _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
     _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
     _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : "");
-    _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
+    _config_id += lower_string(string_from_data_type(src0->data_type()));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(1));
+    _config_id += support::cpp11::to_string(dst->dimension(1));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(0));
+    _config_id += support::cpp11::to_string(dst->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(gemm_info.k);
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(2));
+    _config_id += support::cpp11::to_string(dst->dimension(2));
     _config_id += "_";
     _config_id += support::cpp11::to_string(lhs_info.m0);
     _config_id += "_";
@@ -326,16 +305,16 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext
     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }
 
-Status CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
+Status ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
                                                            const GEMMLHSMatrixInfo &lhs_info,
                                                            const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
 {
     ElementsProcessed num_elements_processed{};
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
-                                                              input1->clone().get(),
-                                                              input2 != nullptr ? input2->clone().get() : nullptr,
-                                                              output->clone().get(),
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(),
+                                                              src1->clone().get(),
+                                                              src2 != nullptr ? src2->clone().get() : nullptr,
+                                                              dst->clone().get(),
                                                               lhs_info,
                                                               rhs_info,
                                                               gemm_info,
@@ -345,15 +324,23 @@ Status CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *in
     return Status{};
 }
 
-void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::CommandQueue &queue)
+void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
-    if(_input1->info()->num_dimensions() < 3)
+    const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+    const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+    const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
+    auto       dst  = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
+    ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr);
+
+    if(src1->info()->num_dimensions() < 3)
     {
         // The stride_z for matrix B must be zero if we do not slice
-        ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);
+        ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
     }
 
     const size_t lhs_idx_batch_size = _reinterpret_input_as_3d && !_has_pad_y ? 3u : 2u;
@@ -368,20 +355,20 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::Co
     slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
 
     // Get cross plane pads
-    const unsigned int total_cross_plane_pad_lhs = _input0->info()->padding().top + _input0->info()->padding().bottom;
-    const unsigned int total_cross_plane_pad_out = _output->info()->padding().top + _output->info()->padding().bottom;
+    const unsigned int total_cross_plane_pad_lhs = src0->info()->padding().top + src0->info()->padding().bottom;
+    const unsigned int total_cross_plane_pad_out = dst->info()->padding().top + dst->info()->padding().bottom;
 
     // The execution should fail if we try to run with has_pad_y = false but we have padding in either the LHS or DST tensor
     ARM_COMPUTE_ERROR_ON(!_has_pad_y && ((total_cross_plane_pad_lhs != 0) || (total_cross_plane_pad_out != 0)));
 
-    cl::Image2D input1_image2d;
+    cl::Image2D src1_image2d;
 
     if(_export_to_cl_image)
     {
-        const TensorShape shape2d(_input1->info()->dimension(0) / 4, _input1->info()->dimension(1) * _input1->info()->dimension(2));
-        const size_t      image_row_pitch = _input1->info()->strides_in_bytes()[1];
+        const TensorShape shape2d(src1->info()->dimension(0) / 4, src1->info()->dimension(1) * src1->info()->dimension(2));
+        const size_t      image_row_pitch = src1->info()->strides_in_bytes()[1];
 
-        input1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input1->cl_buffer(), shape2d, _input1->info()->data_type(), image_row_pitch);
+        src1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), src1->cl_buffer(), shape2d, src1->info()->data_type(), image_row_pitch);
     }
 
     do
@@ -397,38 +384,38 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::Co
         unsigned int idx = 0;
 
         // LHS buffer
-        add_2D_tensor_argument(idx, _input0, slice);
+        add_2D_tensor_argument(idx, src0, slice);
 
         // RHS buffer or RHS OpenCL image (_export_to_cl_image == true)
         if(_export_to_cl_image)
         {
-            _kernel.setArg(idx++, input1_image2d);
+            _kernel.setArg(idx++, src1_image2d);
         }
         else
         {
-            add_2D_tensor_argument(idx, _input1, slice_b);
+            add_2D_tensor_argument(idx, src1, slice_b);
         }
 
         // Bias buffer (_add_bias == true)
-        add_2D_tensor_argument_if(_add_bias, idx, _input2, slice);
+        add_2D_tensor_argument_if(_add_bias, idx, src2, slice);
 
-        // Output buffer
-        add_2D_tensor_argument(idx, _output, slice);
+        // dst buffer
+        add_2D_tensor_argument(idx, dst, slice);
 
         // LHS stride_z
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[lhs_idx_batch_size]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[lhs_idx_batch_size]));
 
         // RHS stride_z (not used if _export_to_cl_image == true)
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[rhs_idx_batch_size]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[rhs_idx_batch_size]));
 
         // Bias stride_z (if _add_bias == true)
         if(_add_bias)
         {
-            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[bia_idx_batch_size]));
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[bia_idx_batch_size]));
         }
 
-        // Output stride_z
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[out_idx_batch_size]));
+        // dst stride_z
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[out_idx_batch_size]));
 
         // Cross-plan padding (if _reinterpret_input_as_3d = true)
         if(_reinterpret_input_as_3d && _has_pad_y)
@@ -436,7 +423,7 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::Co
             _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad_lhs));
         }
 
-        // Cross-plan padding (if _reinterpret_output_as_3d = true)
+        // Cross-plan padding (if reinterpret_output_as_3d = true)
         if(_reinterpret_output_as_3d && _has_pad_y)
         {
             _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad_out));
@@ -446,4 +433,6 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::Co
     }
     while(window.slide_window_slice_3D(slice));
 }
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h
new file mode 100644
index 0000000000..ff6c391e15
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2019-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_ONLY_RHS_KERNEL_H
+#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_ONLY_RHS_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/core/gpu/cl/IClKernel.h"
+
+#include "arm_compute/core/KernelDescriptors.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+/** OpenCL kernel to multiply matrices when only the input matrix RHS (src1) has been reshaped
+ *
+ * @note The input matrix src1 must be reshaped through @ref ClGemmReshapeRhsMatrixKernel
+ */
+class ClGemmMatrixMultiplyReshapedOnlyRhsKernel : public ICLKernel
+{
+public:
+    ClGemmMatrixMultiplyReshapedOnlyRhsKernel() = default;
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyReshapedOnlyRhsKernel);
+    /** Initialise the kernel's input and output.
+     *
+     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function.
+     *       Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer,
+     *       the following conditions are required:
+     *       -# rhs_info.n0 can only be 4, 8 and 16
+     *       -# rhs_info.k0 can only be 4, 8 and 16
+     *       -# Data type can only be F32
+     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
+     *       -# The stride Y for the src1 should satisfy the OpenCL pitch alignment requirement
+     *       -# src1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
+     *       -# src1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
+     *
+     * @param[in]  compile_context The compile context to be used.
+     * @param[in]  src0            Input tensor containing the LHS matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true).
+     *                             The number of dimensions for the LHS matrix must be less or equal than 4.
+     * @param[in]  src1            Input tensor containing the RHS reshaped matrix. Data type supported: same as @p src0. The number of dimensions for the RHS matrix must be less or equal than 3.
+     * @param[in]  src2            Input tensor containing the bias matrix. Data type supported: same as @p src0.
+     * @param[out] dst             Output tensor to store the result of matrix multiplication. Data type supported: same as @p src0
+     * @param[in]  alpha           Weight of the matrix product
+     * @param[in]  beta            Weight of the matrix bias
+     * @param[in]  lhs_info        LHS matrix information used to retrieve the number of rows to be processed by each thread. Only the following values are supported:
+     *                             lhs_info.m0: 1,2,3,4,5,6,7,8
+     * @param[in]  rhs_info        RHS matrix information used for reshaping the src1 tensor.  Only the following values are supported:
+     *                             rhs_info.k0: 2,3,4,8,16
+     *                             rhs_info.n0: 2,3,4,8,16
+     *                             rhs_info.transpose: true,false
+     * @param[in]  gemm_info       GEMM information used to retrieve the original dimensions of the input matrices
+     */
+    void configure(const ClCompileContext &compile_context,
+                   ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta,
+                   const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info);
+    /** Static function to check if given info will lead to a valid configuration
+     *
+     * Similar to @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
+                           const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info);
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+
+private:
+    bool _slide_matrix_b{ true };
+    bool _reinterpret_input_as_3d{ false };
+    bool _reinterpret_output_as_3d{ false };
+    bool _use_dummy_work_items{ false };
+    bool _add_bias{ false };
+    bool _export_to_cl_image{ false };
+    bool _has_pad_y{ false };
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_ONLY_RHS_KERNEL_H */
diff --git a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.cpp b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.cpp
index cc95315894..98161edfff 100644
--- a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
@@ -35,17 +35,20 @@
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
+#include "support/Cast.h"
 #include "support/StringSupport.h"
 
 namespace arm_compute
 {
-using namespace arm_compute::misc::shape_calculator;
-
+namespace opencl
+{
+namespace kernels
+{
 namespace
 {
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.v0 == 0);
@@ -53,50 +56,51 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 > 16);
     ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 2 || lhs_info.m0 > 8);
 
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
-    ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN);
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
+    ARM_COMPUTE_RETURN_ERROR_ON(src->data_type() == DataType::UNKNOWN);
 
-    if(output->total_size() != 0)
+    if(dst->total_size() != 0)
     {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_lhs_reshaped_shape(*input, lhs_info, reinterpret_input_as_3d));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(),
+                                                           misc::shape_calculator::compute_lhs_reshaped_shape(*src, lhs_info, reinterpret_input_as_3d));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(src, dst);
     }
 
     return Status{};
 }
 
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
 {
     const unsigned int num_elems_processed_per_iteration_x = lhs_info.k0;
     const unsigned int num_elems_processed_per_iteration_y = lhs_info.m0;
     bool               window_changed                      = false;
 
-    TensorInfo tmp_info(*input);
+    TensorInfo tmp_info(*src);
 
     if(reinterpret_input_as_3d)
     {
-        // Since the input tensor has to be reinterpreted as 3D and the execute window is based on a 2D interleave,
+        // Since the src tensor has to be reinterpreted as 3D and the execute window is based on a 2D interleave,
         // the window needs to be constructed on the 2D collapsed version of the tensor
-        TensorShape tmp_shape(input->tensor_shape());
+        TensorShape tmp_shape(src->tensor_shape());
         tmp_shape.collapse(2U, 1U);
         tmp_info.set_tensor_shape(tmp_shape);
     }
 
-    // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*input, lhs_info, reinterpret_input_as_3d)));
+    // dst auto inizialitation if not yet initialized
+    auto_init_if_empty(*dst, src->clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(*src, lhs_info, reinterpret_input_as_3d)));
 
     // Configure window
     Window win    = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-    Window win_in = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+    Window win_in = calculate_max_window(*src, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
 
-    AccessWindowStatic input_access(input, 0, 0,
-                                    input->dimension(0),
-                                    input->dimension(1));
-    AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1));
+    AccessWindowStatic src_access(src, 0, 0,
+                                  src->dimension(0),
+                                  src->dimension(1));
+    AccessWindowStatic dst_access(dst, 0, 0, dst->dimension(0), dst->dimension(1));
 
-    window_changed = update_window_and_padding(win_in, input_access) || // window used by the execute_window_loop
-                     update_window_and_padding(win, output_access);     // window used to update the padding requirements of output tensor
+    window_changed = update_window_and_padding(win_in, src_access) || // window used by the execute_window_loop
+                     update_window_and_padding(win, dst_access);      // window used to update the padding requirements of dst tensor
 
     // Collapse along the Z direction
     // This collapse needs to be here in order to tune the Z dimension of LWS
@@ -107,31 +111,19 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
 }
 } // namespace
 
-CLGEMMReshapeLHSMatrixKernel::CLGEMMReshapeLHSMatrixKernel()
-    : _input(nullptr), _output(nullptr), _reinterpret_input_as_3d(false)
+void ClGemmReshapeLhsMatrixKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
 {
-}
-
-void CLGEMMReshapeLHSMatrixKernel::configure(const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input, output, lhs_info, reinterpret_input_as_3d);
-}
-
-void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
 
     // Perform validate step
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), lhs_info, reinterpret_input_as_3d));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, lhs_info, reinterpret_input_as_3d));
 
-    auto padding_info = get_padding_info({ input });
+    auto padding_info = get_padding_info({ src });
 
-    _input                   = input;
-    _output                  = output;
     _reinterpret_input_as_3d = reinterpret_input_as_3d;
 
-    const unsigned int src_w           = input->info()->dimension(0);
-    const unsigned int src_h           = _reinterpret_input_as_3d ? input->info()->dimension(1) * input->info()->dimension(2) : input->info()->dimension(1);
+    const unsigned int src_w           = src->dimension(0);
+    const unsigned int src_h           = _reinterpret_input_as_3d ? src->dimension(1) * src->dimension(2) : src->dimension(1);
     const unsigned int partial_load_m0 = src_h % lhs_info.m0;
     const unsigned int partial_load_k0 = src_w % lhs_info.k0;
 
@@ -144,9 +136,9 @@ void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_con
     build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src_h));
     build_opts.add_option_if(lhs_info.interleave, "-DINTERLEAVE");
     build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
-    build_opts.add_option_if(_reinterpret_input_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(input->info()->dimension(1)));
-    build_opts.add_option_if(_reinterpret_input_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(input->info()->dimension(2)));
-    build_opts.add_option("-DDATA_TYPE=" + get_cl_unsigned_type_from_element_size(input->info()->element_size()));
+    build_opts.add_option_if(_reinterpret_input_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(src->dimension(1)));
+    build_opts.add_option_if(_reinterpret_input_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(src->dimension(2)));
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_unsigned_type_from_element_size(src->element_size()));
     build_opts.add_option("-DPARTIAL_LOAD_M0=" + support::cpp11::to_string(partial_load_m0));
     build_opts.add_option("-DPARTIAL_LOAD_K0=" + support::cpp11::to_string(partial_load_k0));
 
@@ -157,20 +149,20 @@ void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_con
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
 
     // Configure kernel window
-    auto win_config = validate_and_configure_window(input->info(), output->info(), lhs_info, reinterpret_input_as_3d);
+    auto win_config = validate_and_configure_window(src, dst, lhs_info, reinterpret_input_as_3d);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
 
     // Set config_id for enabling LWS tuning
     _config_id = "gemm_reshape_lhs_matrix_";
     _config_id += (_reinterpret_input_as_3d ? "3d_" : "");
-    _config_id += lower_string(string_from_data_type(input->info()->data_type()));
+    _config_id += lower_string(string_from_data_type(src->data_type()));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(0));
+    _config_id += support::cpp11::to_string(dst->dimension(0));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(1));
+    _config_id += support::cpp11::to_string(dst->dimension(1));
     _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(2));
+    _config_id += support::cpp11::to_string(dst->dimension(2));
     _config_id += "_";
     _config_id += support::cpp11::to_string(lhs_info.m0);
     _config_id += "_";
@@ -185,36 +177,43 @@ void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_con
     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }
 
-Status CLGEMMReshapeLHSMatrixKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
+Status ClGemmReshapeLhsMatrixKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
 {
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, lhs_info, reinterpret_input_as_3d));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), lhs_info, reinterpret_input_as_3d).first);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, lhs_info, reinterpret_input_as_3d));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), lhs_info, reinterpret_input_as_3d).first);
 
     return Status{};
 }
 
-void CLGEMMReshapeLHSMatrixKernel::run(const Window &window, cl::CommandQueue &queue)
+void ClGemmReshapeLhsMatrixKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
+    const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
+    auto       dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+
     Window slice = window.first_slice_window_3D();
 
     if(_reinterpret_input_as_3d)
     {
-        // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
+        // Pass bottom paddings to the kernel if the src has to be reinterpreted as 3D tensor
         const unsigned int idx0                  = 2 * num_arguments_per_3D_tensor();
-        const unsigned int total_cross_plane_pad = _input->info()->padding().top + _input->info()->padding().bottom;
+        const unsigned int total_cross_plane_pad = src->info()->padding().top + src->info()->padding().bottom;
         _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
     }
 
     do
     {
         unsigned int idx = 0;
-        add_3D_tensor_argument(idx, _input, slice);
-        add_3D_tensor_argument(idx, _output, slice);
+        add_3D_tensor_argument(idx, src, slice);
+        add_3D_tensor_argument(idx, dst, slice);
         enqueue(queue, *this, slice, lws_hint());
     }
     while(window.slide_window_slice_3D(slice));
 }
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h
new file mode 100644
index 0000000000..b830ba02b4
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2018-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_GEMM_RESHAPE_LHS_MATRIX_KERNEL_H
+#define ARM_COMPUTE_CL_GEMM_RESHAPE_LHS_MATRIX_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/core/gpu/cl/IClKernel.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+/** OpenCL kernel to reshape the LHS matrix when performing the matrix multiplication.
+ *  In particular, this function splits the src matrix in blocks of size M0xK0 (defined through GEMMLHSInfo) and
+ *  stores each one in the dst matrix unrolling the values
+ */
+class ClGemmReshapeLhsMatrixKernel : public ICLKernel
+{
+public:
+    ClGemmReshapeLhsMatrixKernel() = default;
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmReshapeLhsMatrixKernel);
+    /** Initialise the kernel's input and output.
+     *
+     * @param[in]  compile_context       The compile context to be used.
+     * @param[in]  src                   Input tensor. Data types supported: All
+     * @param[out] dst                   Output tensor. Data type supported: same as @p src
+     * @param[in]  lhs_info              LHS matrix information to be used for reshaping. This object contains all the necessary
+     *                                   information to reshape the src tensor. Only the following values are supported:
+     *                                   lhs_info.m0: 2,3,4,5,6,7,8
+     *                                   lhs_info.k0: 2,3,4,8,16
+     *                                   lhs_info.v0: greater than 0
+     *                                   lhs_info.transpose: true, false
+     *                                   lhs_info.interleave: true, false
+     * @param[in]  reinterpret_src_as_3d (Optional) True if the src has to be reinterpreted as 3D tensor
+     */
+    void configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_src_as_3d = false);
+    /** Static function to check if given info will lead to a valid configuration
+     *
+     * Similar to @ref ClGemmReshapeLhsMatrixKernel::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_src_as_3d);
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+
+private:
+    bool _reinterpret_input_as_3d{ false };
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CL_GEMM_RESHAPE_LHS_MATRIX_KERNEL_H */
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.cpp b/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.cpp
index 1c4092c0e5..e1ef7c61aa 100644
--- a/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
@@ -33,18 +33,21 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLValidate.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
+#include "support/Cast.h"
 #include "support/StringSupport.h"
 
 namespace arm_compute
 {
-using namespace arm_compute::misc::shape_calculator;
-
+namespace opencl
+{
+namespace kernels
+{
 namespace
 {
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const GEMMRHSMatrixInfo &rhs_info)
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.n0 == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 == 0);
@@ -55,44 +58,44 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
     ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16);
     ARM_COMPUTE_RETURN_ERROR_ON((rhs_info.k0 == 1) && (rhs_info.transpose));
 
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
-    ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN);
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
+    ARM_COMPUTE_RETURN_ERROR_ON(src->data_type() == DataType::UNKNOWN);
 
     if(rhs_info.export_to_cl_image)
     {
-        const TensorInfo tensor_reshaped_info(compute_rhs_reshaped_shape(*input, rhs_info), 1, input->data_type());
-        ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(tensor_reshaped_info, rhs_info));
+        const TensorInfo tensor_reshaped_info(misc::shape_calculator::compute_rhs_reshaped_shape(*src, rhs_info), 1, src->data_type());
+        ARM_COMPUTE_RETURN_ON_ERROR(gemm::validate_image2d_support_on_rhs(tensor_reshaped_info, rhs_info));
     }
 
-    if(output->total_size() != 0)
+    if(dst->total_size() != 0)
     {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_rhs_reshaped_shape(*input, rhs_info));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), misc::shape_calculator::compute_rhs_reshaped_shape(*src, rhs_info));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(src, dst);
     }
 
     return Status{};
 }
 
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const GEMMRHSMatrixInfo &rhs_info)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info)
 {
     const unsigned int num_elems_processed_per_iteration_x = rhs_info.n0;
     const unsigned int num_elems_processed_per_iteration_y = rhs_info.k0;
     bool               window_changed                      = false;
 
-    // Output auto initialization if not yet initialized
-    auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*input, rhs_info)));
+    // dst auto initialization if not yet initialized
+    auto_init_if_empty(*dst, src->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(*src, rhs_info)));
 
     // Configure window
-    Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+    Window win = calculate_max_window(*src, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
 
-    AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+    AccessWindowRectangle src_access(src, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
 
-    window_changed = update_window_and_padding(win, input_access);
+    window_changed = update_window_and_padding(win, src_access);
 
     if(rhs_info.export_to_cl_image)
     {
-        arm_compute::cl_gemm::update_padding_for_cl_image(output);
+        gemm::update_padding_for_cl_image(dst);
     }
 
     // Collapse along the Z direction
@@ -104,25 +107,12 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
 }
 } // namespace
 
-CLGEMMReshapeRHSMatrixKernel::CLGEMMReshapeRHSMatrixKernel()
-    : _input(nullptr), _output(nullptr)
-{
-}
-
-void CLGEMMReshapeRHSMatrixKernel::configure(const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info)
+void ClGemmReshapeRhsMatrixKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info)
 {
-    configure(CLKernelLibrary::get().get_compile_context(), input, output, rhs_info);
-}
-
-void CLGEMMReshapeRHSMatrixKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
 
     // Perform validate step
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), rhs_info));
-
-    _input  = input;
-    _output = output;
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, rhs_info));
 
     // Create build options
     CLBuildOptions build_opts;
@@ -131,8 +121,8 @@ void CLGEMMReshapeRHSMatrixKernel::configure(const CLCompileContext &compile_con
     build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
     build_opts.add_option_if(rhs_info.transpose, "-DTRANSPOSE");
     build_opts.add_option_if(rhs_info.interleave, "-DINTERLEAVE");
-    build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
-    build_opts.add_option("-DDATA_TYPE=" + get_cl_unsigned_type_from_element_size(input->info()->element_size()));
+    build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(1)));
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_unsigned_type_from_element_size(src->element_size()));
 
     std::string kernel_name("gemm_reshape_rhs_matrix_");
     kernel_name += rhs_info.transpose ? "t" : "nt";
@@ -141,33 +131,40 @@ void CLGEMMReshapeRHSMatrixKernel::configure(const CLCompileContext &compile_con
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
 
     // Configure kernel window
-    auto win_config = validate_and_configure_window(input->info(), output->info(), rhs_info);
+    auto win_config = validate_and_configure_window(src, dst, rhs_info);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
 }
 
-Status CLGEMMReshapeRHSMatrixKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const GEMMRHSMatrixInfo &rhs_info)
+Status ClGemmReshapeRhsMatrixKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info)
 {
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, rhs_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), rhs_info).first);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, rhs_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), rhs_info).first);
 
     return Status{};
 }
 
-void CLGEMMReshapeRHSMatrixKernel::run(const Window &window, cl::CommandQueue &queue)
+void ClGemmReshapeRhsMatrixKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
+    const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
+    auto       dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+
     Window slice = window.first_slice_window_3D();
 
     do
     {
         unsigned int idx = 0;
-        add_3D_tensor_argument(idx, _input, slice);
-        add_3D_tensor_argument(idx, _output, slice);
+        add_3D_tensor_argument(idx, src, slice);
+        add_3D_tensor_argument(idx, dst, slice);
         enqueue(queue, *this, slice, lws_hint());
     }
     while(window.slide_window_slice_3D(slice));
 }
-} // namespace arm_compute
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h b/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h
new file mode 100644
index 0000000000..e877d87408
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2018-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_GEMM_RESHAPE_RHS_MATRIX_KERNEL_H
+#define ARM_COMPUTE_CL_GEMM_RESHAPE_RHS_MATRIX_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/core/gpu/cl/IClKernel.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+/** OpenCL kernel to reshape the RHS matrix when performing the matrix multiplication
+ *  In particular, this kernel splits the src matrix in blocks of size K0xN0 and stores each one in
+ *  the dst matrix unrolling the values */
+class ClGemmReshapeRhsMatrixKernel : public ICLKernel
+{
+public:
+    ClGemmReshapeRhsMatrixKernel() = default;
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmReshapeRhsMatrixKernel);
+    /** Initialise the kernel's input and output.
+     *
+     * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will guarantee the OpenCL pitch alignment for the output tensor,
+     *       required to create a OpenCL image object from buffer in @ref ClGemmMatrixMultiplyReshapedKernel and in @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel
+     *       Since the OpenCL image object is created importing the OpenCL buffer, the following conditions are required:
+     *       -# rhs_info.n0 can only be 4, 8 and 16
+     *       -# rhs_info.k0 can only be 4, 8 and 16
+     *       -# Data type can only be F32, F16
+     *       -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension
+     *       -# output width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4)
+     *       -# output (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT
+     *       -# The output tensor should be only consumed by @ref ClGemmMatrixMultiplyReshapedKernel or @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel
+     *
+     * @param[in]  compile_context The compile context to be used.
+     * @param[in]  src             Input tensor. Data types supported: All
+     * @param[out] dst             Output tensor. Data type supported: same as @p src
+     * @param[in]  rhs_info        RHS matrix information to be used for reshaping. This object contains all the necessary
+     *                             information to reshape the src tensor. Only the following values are supported:
+     *                             rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image == true)
+     *                             rhs_info.k0: 1,2,3,4,8,16 (k0 = 1 only if rhs_info.transpose = false), (only 4, 8 and 16 if rhs_info.export_to_cl_image == true)
+     *                             rhs_info.h0: greater than 0
+     *                             rhs_info.transpose: true, false
+     *                             rhs_info.interleave: true, false
+     */
+    void configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info);
+    /** Static function to check if given info will lead to a valid configuration
+     *
+     * Similar to @ref ClGemmReshapeRhsMatrixKernel::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info);
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CL_GEMM_RESHAPE_RHS_MATRIX_KERNEL_H */
\ No newline at end of file
diff --git a/src/core/CL/gemm/CLGEMMHelpers.cpp b/src/core/gpu/cl/kernels/gemm/ClGemmHelpers.cpp
index 61aa962198..0a8ba971ed 100644
--- a/src/core/CL/gemm/CLGEMMHelpers.cpp
+++ b/src/core/gpu/cl/kernels/gemm/ClGemmHelpers.cpp
@@ -21,22 +21,23 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/ITensorInfo.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 
 #include <utility>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
-using namespace arm_compute::misc::shape_calculator;
-
 std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_lhs_rhs_info(unsigned int m, unsigned int n, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
                                                                        bool lhs_interleave, bool rhs_interleave, bool lhs_transpose, bool rhs_transpose, bool export_to_cl_image)
 {
@@ -55,7 +56,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> select_lhs_rhs_info(std::pair<GE
                                                                     unsigned int n, unsigned int k, unsigned int b, DataType data_type)
 {
     const TensorInfo  tensor_rhs_info(TensorShape(n, k, b), 1, data_type);
-    const TensorShape shape = compute_rhs_reshaped_shape(tensor_rhs_info, info_img.second);
+    const TensorShape shape = misc::shape_calculator::compute_rhs_reshaped_shape(tensor_rhs_info, info_img.second);
     const TensorInfo  tensor_reshaped_info(shape, 1, data_type);
 
     if(bool(validate_image2d_support_on_rhs(tensor_reshaped_info, info_img.second)))
@@ -73,7 +74,7 @@ void update_padding_for_cl_image(ITensorInfo *tensor)
     constexpr unsigned int num_floats_per_pixel = 4;
 
     const unsigned int stride_y_in_elements = tensor->strides_in_bytes()[1] / tensor->element_size();
-    const unsigned int pixel_alignment       = get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device());
+    const unsigned int pixel_alignment      = get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device());
 
     ARM_COMPUTE_ERROR_ON_MSG(pixel_alignment == 0, "Cannot retrieve cl_image pitch alignment");
     if(pixel_alignment == 0)
@@ -109,5 +110,7 @@ Status validate_image2d_support_on_rhs(const ITensorInfo &tensor_reshaped_info,
 
     return Status{};
 }
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/CL/gemm/CLGEMMHelpers.h b/src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h
index 57624673c0..3fce8c9173 100644
--- a/src/core/CL/gemm/CLGEMMHelpers.h
+++ b/src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,18 +21,19 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMHELPERS_H
-#define ARM_COMPUTE_CLGEMMHELPERS_H
+#ifndef ARM_COMPUTE_CL_GEMM_HELPERS_H
+#define ARM_COMPUTE_CL_GEMM_HELPERS_H
 
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 
 namespace arm_compute
 {
-class ITensorInfo;
-struct GEMMRHSMatrixInfo;
-
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** Configure @ref GEMMLHSMatrixInfo and @ref GEMMRHSMatrixInfo
  *
@@ -87,6 +88,8 @@ void update_padding_for_cl_image(ITensorInfo *tensor);
  * @return Status reporting if we can use the image2d OpenCL object on the RHS reshaped matrix
  */
 Status validate_image2d_support_on_rhs(const ITensorInfo &tensor_reshaped_info, const GEMMRHSMatrixInfo &rhs_info);
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMHELPERS_H */
+#endif /* ARM_COMPUTE_CL_GEMM_HELPERS_H */
diff --git a/src/core/CL/ICLGEMMKernelConfiguration.h b/src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h
index 886905ecd0..a49836cfda 100644
--- a/src/core/CL/ICLGEMMKernelConfiguration.h
+++ b/src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h
@@ -21,15 +21,22 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_ICLGEMMKERNELCONFIGURATION_H
-#define ARM_COMPUTE_ICLGEMMKERNELCONFIGURATION_H
+#ifndef ARM_COMPUTE_ICL_GEMM_KERNEL_CONFIG_H
+#define ARM_COMPUTE_ICL_GEMM_KERNEL_CONFIG_H
 
 #include "arm_compute/core/GPUTarget.h"
 #include "arm_compute/core/Types.h"
+#include "src/core/common/Macros.h"
 
 #include <array>
 namespace arm_compute
 {
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
+{
 /** Basic container for the OpenCL GEMM configuration functions */
 template <class T>
 class CLGEMMConfigArray
@@ -82,27 +89,20 @@ private:
 };
 
 /** Basic interface for the GEMM kernel configuration */
-class ICLGEMMKernelConfiguration
+class IClGemmKernelConfig
 {
 public:
     /** Constructor
      *
      * @param[in] arch GPU target
      */
-    ICLGEMMKernelConfiguration(GPUTarget arch)
+    IClGemmKernelConfig(GPUTarget arch)
         : _target(arch)
     {
     }
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    ICLGEMMKernelConfiguration(const ICLGEMMKernelConfiguration &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    ICLGEMMKernelConfiguration &operator=(const ICLGEMMKernelConfiguration &) = delete;
-    /** Default Move Constructor. */
-    ICLGEMMKernelConfiguration(ICLGEMMKernelConfiguration &&) = default;
-    /** Default move assignment operator */
-    ICLGEMMKernelConfiguration &operator=(ICLGEMMKernelConfiguration &&) = default;
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(IClGemmKernelConfig);
     /** Virtual destructor */
-    virtual ~ICLGEMMKernelConfiguration() = default;
+    virtual ~IClGemmKernelConfig() = default;
     /** Given M, N, K and B, this method returns the @ref GEMMLHSMatrixInfo and @ref GEMMRHSMatrixInfo to be used
      *
      * @param[in] m         Number of rows LHS matrix
@@ -116,5 +116,8 @@ public:
 protected:
     GPUTarget _target;
 };
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_ICLGEMMKERNELCONFIGURATION_H */
+#endif /* ARM_COMPUTE_ICL_GEMM_KERNEL_CONFIG_H */
diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.cpp b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp
index 52023dd835..9d11006703 100644
--- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.cpp
+++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp
@@ -21,40 +21,44 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h"
+#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/GPUTarget.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <utility>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
 {
-CLGEMMDefaultConfigNativeBifrost::CLGEMMDefaultConfigNativeBifrost(GPUTarget gpu)
-    : ICLGEMMKernelConfiguration(gpu)
+namespace kernels
+{
+namespace gemm
+{
+ClGemmDefaultConfigNativeBifrost::ClGemmDefaultConfigNativeBifrost(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
 {
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
 {
-    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigNativeBifrost::*)(unsigned int m, unsigned int n, unsigned int k,
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigNativeBifrost::*)(unsigned int m, unsigned int n, unsigned int k,
                                              unsigned int b);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G71(&CLGEMMDefaultConfigNativeBifrost::configure_G71_f32,
-                                                                    &CLGEMMDefaultConfigNativeBifrost::configure_G71_f32, // We use the F32 heuristic
-                                                                    &CLGEMMDefaultConfigNativeBifrost::configure_G71_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G71(&ClGemmDefaultConfigNativeBifrost::configure_G71_f32,
+                                                                    &ClGemmDefaultConfigNativeBifrost::configure_G71_f32, // We use the F32 heuristic
+                                                                    &ClGemmDefaultConfigNativeBifrost::configure_G71_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&CLGEMMDefaultConfigNativeBifrost::configure_G76_f32,
-                                                                    &CLGEMMDefaultConfigNativeBifrost::configure_G76_f32, // We use the F32 heuristic
-                                                                    &CLGEMMDefaultConfigNativeBifrost::configure_G76_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&ClGemmDefaultConfigNativeBifrost::configure_G76_f32,
+                                                                    &ClGemmDefaultConfigNativeBifrost::configure_G76_f32, // We use the F32 heuristic
+                                                                    &ClGemmDefaultConfigNativeBifrost::configure_G76_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&CLGEMMDefaultConfigNativeBifrost::configure_default_f32,
-                                                                    &CLGEMMDefaultConfigNativeBifrost::configure_default_f32, // We use the F32 heuristic
-                                                                    &CLGEMMDefaultConfigNativeBifrost::configure_default_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&ClGemmDefaultConfigNativeBifrost::configure_default_f32,
+                                                                    &ClGemmDefaultConfigNativeBifrost::configure_default_f32, // We use the F32 heuristic
+                                                                    &ClGemmDefaultConfigNativeBifrost::configure_default_u8);
 
     ConfigurationFunctionExecutorPtr func = nullptr;
 
@@ -75,7 +79,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost
     return (this->*func)(m, n, k, b);
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_G71_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_G71_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -101,7 +105,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_G71_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_G71_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -155,7 +159,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -188,7 +192,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -221,7 +225,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_default_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_default_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -229,12 +233,14 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost
     return configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 1, false, false, false, false);
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_default_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_default_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
 
     return configure_lhs_rhs_info(m, n, 5, 2, 16, 1, 1, false, false, false, false);
 }
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h
index 78d47a8195..385b96e40e 100644
--- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h
+++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,24 +21,28 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEBIFROST_H
-#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEBIFROST_H
+#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_BIFROST_H
+#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_BIFROST_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** Bifrost based OpenCL GEMMNative configuration */
-class CLGEMMDefaultConfigNativeBifrost final : public ICLGEMMKernelConfiguration
+class ClGemmDefaultConfigNativeBifrost final : public IClGemmKernelConfig
 {
 public:
     /** Constructor
      *
      * @param[in] gpu GPU target
      */
-    CLGEMMDefaultConfigNativeBifrost(GPUTarget gpu);
+    ClGemmDefaultConfigNativeBifrost(GPUTarget gpu);
 
     // Inherited overridden method
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override;
@@ -51,6 +55,8 @@ private:
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_default_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_default_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEBIFROST_H */
+#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_BIFROST_H */
diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.cpp b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.cpp
index cf9bb1828f..e3c129e3be 100644
--- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.cpp
+++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.cpp
@@ -21,39 +21,43 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h"
+#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/GPUTarget.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <utility>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
 {
-CLGEMMDefaultConfigNativeMidgard::CLGEMMDefaultConfigNativeMidgard(GPUTarget gpu)
-    : ICLGEMMKernelConfiguration(gpu)
+namespace kernels
+{
+namespace gemm
+{
+ClGemmDefaultConfigNativeMidgard::ClGemmDefaultConfigNativeMidgard(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
 {
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeMidgard::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeMidgard::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
 {
-    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigNativeMidgard::*)(unsigned int m, unsigned int n, unsigned int k,
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigNativeMidgard::*)(unsigned int m, unsigned int n, unsigned int k,
                                              unsigned int b);
 
     CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_default(nullptr,
                                                                         nullptr,
-                                                                        &CLGEMMDefaultConfigNativeMidgard::default_q8);
+                                                                        &ClGemmDefaultConfigNativeMidgard::default_q8);
 
     auto func = configs_default.get_function(data_type);
     ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not support for GEMM");
     return (this->*func)(m, n, k, b);
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeMidgard::default_q8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeMidgard::default_q8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -63,5 +67,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeMidgard
 
     return configure_lhs_rhs_info(m, n, m0, n0, 2, 1, 1, false, false, false, false);
 }
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h
index 40c91d42b1..0ff5471f7c 100644
--- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h
+++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020 Arm Limited.
+ * Copyright (c) 2020-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,24 +21,28 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEMIDGARD_H
-#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEMIDGARD_H
+#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_MIDGARD_H
+#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_MIDGARD_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** Midgard based OpenCL GEMMNative configuration */
-class CLGEMMDefaultConfigNativeMidgard final : public ICLGEMMKernelConfiguration
+class ClGemmDefaultConfigNativeMidgard final : public IClGemmKernelConfig
 {
 public:
     /** Constructor
      *
      * @param[in] gpu GPU target
      */
-    CLGEMMDefaultConfigNativeMidgard(GPUTarget gpu);
+    ClGemmDefaultConfigNativeMidgard(GPUTarget gpu);
 
     // Inherited overridden method
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override;
@@ -46,6 +50,8 @@ public:
 private:
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> default_q8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEMIDGARD_H */
+#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_MIDGARD_H */
diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.cpp b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.cpp
index 3b55be747f..92767aca52 100644
--- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.cpp
+++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.cpp
@@ -21,39 +21,43 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h"
+#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/GPUTarget.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <utility>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
 {
-CLGEMMDefaultConfigNativeValhall::CLGEMMDefaultConfigNativeValhall(GPUTarget gpu)
-    : ICLGEMMKernelConfiguration(gpu)
+namespace kernels
+{
+namespace gemm
+{
+ClGemmDefaultConfigNativeValhall::ClGemmDefaultConfigNativeValhall(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
 {
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
 {
-    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigNativeValhall::*)(unsigned int m, unsigned int n, unsigned int k,
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigNativeValhall::*)(unsigned int m, unsigned int n, unsigned int k,
                                              unsigned int b);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_default(&CLGEMMDefaultConfigNativeValhall::configure_G77_f32,
-                                                                        &CLGEMMDefaultConfigNativeValhall::configure_G77_f16,
-                                                                        &CLGEMMDefaultConfigNativeValhall::configure_G77_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_default(&ClGemmDefaultConfigNativeValhall::configure_G77_f32,
+                                                                        &ClGemmDefaultConfigNativeValhall::configure_G77_f16,
+                                                                        &ClGemmDefaultConfigNativeValhall::configure_G77_u8);
 
     auto func = configs_default.get_function(data_type);
     ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not support for GEMM");
     return (this->*func)(m, n, k, b);
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -79,7 +83,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -105,7 +109,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -158,5 +162,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall
         }
     }
 }
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h
index 08d2d57a3e..17e4c9d339 100644
--- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h
+++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020 Arm Limited.
+ * Copyright (c) 2020-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,24 +21,28 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEVALHALL_H
-#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEVALHALL_H
+#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_VALHALL_H
+#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_VALHALL_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** Valhall based OpenCL GEMMNative configuration */
-class CLGEMMDefaultConfigNativeValhall final : public ICLGEMMKernelConfiguration
+class ClGemmDefaultConfigNativeValhall final : public IClGemmKernelConfig
 {
 public:
     /** Constructor
      *
      * @param[in] gpu GPU target
      */
-    CLGEMMDefaultConfigNativeValhall(GPUTarget gpu);
+    ClGemmDefaultConfigNativeValhall(GPUTarget gpu);
 
     // Inherited overridden method
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override;
@@ -48,6 +52,8 @@ private:
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEVALHALL_H */
+#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_VALHALL_H */
diff --git a/src/core/CL/gemm/native/CLGEMMNativeKernelConfiguration.h b/src/core/gpu/cl/kernels/gemm/native/ClGemmNativeKernelConfig.h
index 39a534e817..ff6a0128af 100644
--- a/src/core/CL/gemm/native/CLGEMMNativeKernelConfiguration.h
+++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmNativeKernelConfig.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,22 +21,26 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMNATIVEKERNELCONFIGURATION_H
-#define ARM_COMPUTE_CLGEMMNATIVEKERNELCONFIGURATION_H
+#ifndef ARM_COMPUTE_CL_GEMM_NATIVE_KERNEL_CONFIGURATION_H
+#define ARM_COMPUTE_CL_GEMM_NATIVE_KERNEL_CONFIGURATION_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
-#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h"
-#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h"
-#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
+#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h"
+#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h"
+#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h"
 
 #include <memory>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** CLGEMMNative factory class */
-class CLGEMMNativeKernelConfigurationFactory final
+class ClGemmNativeKernelConfigurationFactory final
 {
 public:
     /** Static method to construct CLGEMMNative kernel object accordingly with the GPU target
@@ -45,21 +49,23 @@ public:
      *
      * @return CLGEMMNative kernel configuration class
      */
-    static std::unique_ptr<ICLGEMMKernelConfiguration> create(GPUTarget gpu)
+    static std::unique_ptr<IClGemmKernelConfig> create(GPUTarget gpu)
     {
         switch(get_arch_from_target(gpu))
         {
             case GPUTarget::MIDGARD:
-                return std::make_unique<CLGEMMDefaultConfigNativeMidgard>(gpu);
+                return std::make_unique<ClGemmDefaultConfigNativeMidgard>(gpu);
             case GPUTarget::BIFROST:
-                return std::make_unique<CLGEMMDefaultConfigNativeBifrost>(gpu);
+                return std::make_unique<ClGemmDefaultConfigNativeBifrost>(gpu);
             case GPUTarget::VALHALL:
-                return std::make_unique<CLGEMMDefaultConfigNativeValhall>(gpu);
+                return std::make_unique<ClGemmDefaultConfigNativeValhall>(gpu);
             default:
                 ARM_COMPUTE_ERROR("Not supported GPU target");
         }
     }
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMNATIVEKERNELCONFIGURATION_H */
+#endif /*ARM_COMPUTE_CL_GEMM_NATIVE_KERNEL_CONFIGURATION_H */
diff --git a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.cpp b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.cpp
index 5877ab96e7..b030913a87 100644
--- a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.cpp
+++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
@@ -29,36 +29,40 @@
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <utility>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 using namespace arm_compute::misc::shape_calculator;
 
-CLGEMMDefaultConfigReshapedBifrost::CLGEMMDefaultConfigReshapedBifrost(GPUTarget gpu)
-    : ICLGEMMKernelConfiguration(gpu)
+ClGemmDefaultConfigReshapedBifrost::ClGemmDefaultConfigReshapedBifrost(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
 {
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
 {
-    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigReshapedBifrost::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedBifrost::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&CLGEMMDefaultConfigReshapedBifrost::configure_G7x_f32,
-                                                                    &CLGEMMDefaultConfigReshapedBifrost::configure_G7x_f16,
-                                                                    &CLGEMMDefaultConfigReshapedBifrost::configure_G7x_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&ClGemmDefaultConfigReshapedBifrost::configure_G7x_f32,
+                                                                    &ClGemmDefaultConfigReshapedBifrost::configure_G7x_f16,
+                                                                    &ClGemmDefaultConfigReshapedBifrost::configure_G7x_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&CLGEMMDefaultConfigReshapedBifrost::configure_G52_f32,
-                                                                    &CLGEMMDefaultConfigReshapedBifrost::configure_G52_f16,
-                                                                    &CLGEMMDefaultConfigReshapedBifrost::configure_G7x_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&ClGemmDefaultConfigReshapedBifrost::configure_G52_f32,
+                                                                    &ClGemmDefaultConfigReshapedBifrost::configure_G52_f16,
+                                                                    &ClGemmDefaultConfigReshapedBifrost::configure_G7x_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&CLGEMMDefaultConfigReshapedBifrost::configure_G76_f32,
-                                                                    &CLGEMMDefaultConfigReshapedBifrost::configure_G76_f16,
-                                                                    &CLGEMMDefaultConfigReshapedBifrost::configure_G76_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&ClGemmDefaultConfigReshapedBifrost::configure_G76_f32,
+                                                                    &ClGemmDefaultConfigReshapedBifrost::configure_G76_f16,
+                                                                    &ClGemmDefaultConfigReshapedBifrost::configure_G76_u8);
 
     ConfigurationFunctionExecutorPtr func = nullptr;
 
@@ -79,7 +83,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
     return (this->*func)(m, n, k, b);
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -94,7 +98,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -109,7 +113,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -138,7 +142,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
     const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
@@ -237,7 +241,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
 
@@ -253,7 +257,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -303,7 +307,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
     const float r_mk     = static_cast<float>(m) / static_cast<float>(k);
@@ -332,7 +336,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -346,5 +350,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro
         return configure_lhs_rhs_info(m, n, 4, 4, 16, 2, 2, false, true, false, true);
     }
 }
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h
index 814b831b69..52e6ce3f48 100644
--- a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h
+++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,24 +21,28 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDBIFROST_H
-#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDBIFROST_H
+#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_BIFROST_H
+#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_BIFROST_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** Bifrost based OpenCL GEMMReshaped configuration */
-class CLGEMMDefaultConfigReshapedBifrost final : public ICLGEMMKernelConfiguration
+class ClGemmDefaultConfigReshapedBifrost final : public IClGemmKernelConfig
 {
 public:
     /** Constructor
      *
      * @param[in] gpu GPU target
      */
-    CLGEMMDefaultConfigReshapedBifrost(GPUTarget gpu);
+    ClGemmDefaultConfigReshapedBifrost(GPUTarget gpu);
 
     // Inherited overridden method
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override;
@@ -53,6 +57,8 @@ private:
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDBIFROST_H */
+#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_BIFROST_H */
diff --git a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.cpp b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.cpp
index b07092ab83..57e42c92b3 100644
--- a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.cpp
+++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.cpp
@@ -21,35 +21,39 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/GPUTarget.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <utility>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
 {
-CLGEMMDefaultConfigReshapedValhall::CLGEMMDefaultConfigReshapedValhall(GPUTarget gpu)
-    : ICLGEMMKernelConfiguration(gpu)
+namespace kernels
+{
+namespace gemm
+{
+ClGemmDefaultConfigReshapedValhall::ClGemmDefaultConfigReshapedValhall(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
 {
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
 {
-    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigReshapedValhall::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedValhall::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&CLGEMMDefaultConfigReshapedValhall::configure_G77_f32,
-                                                                    &CLGEMMDefaultConfigReshapedValhall::configure_G77_f16,
-                                                                    &CLGEMMDefaultConfigReshapedValhall::configure_G77_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&ClGemmDefaultConfigReshapedValhall::configure_G77_f32,
+                                                                    &ClGemmDefaultConfigReshapedValhall::configure_G77_f16,
+                                                                    &ClGemmDefaultConfigReshapedValhall::configure_G77_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&CLGEMMDefaultConfigReshapedValhall::configure_G78_f32,
-                                                                    &CLGEMMDefaultConfigReshapedValhall::configure_G78_f16,
-                                                                    &CLGEMMDefaultConfigReshapedValhall::configure_G77_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&ClGemmDefaultConfigReshapedValhall::configure_G78_f32,
+                                                                    &ClGemmDefaultConfigReshapedValhall::configure_G78_f16,
+                                                                    &ClGemmDefaultConfigReshapedValhall::configure_G77_u8);
 
     ConfigurationFunctionExecutorPtr func = nullptr;
 
@@ -68,7 +72,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha
     return (this->*func)(m, n, k, b);
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -83,7 +87,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -208,7 +212,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
     const float r_mk     = static_cast<float>(m) / static_cast<float>(k);
@@ -449,7 +453,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
     const float r_nk     = static_cast<float>(n) / static_cast<float>(k);
@@ -514,7 +518,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -528,5 +532,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha
         return configure_lhs_rhs_info(m, n, 4, 4, 16, 2, 2, 0, 1, 0, 1);
     }
 }
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h
index 52b83b09b6..588cd64e0e 100644
--- a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h
+++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h
@@ -21,24 +21,28 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDVALHALL_H
-#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDVALHALL_H
+#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_VALHALL_H
+#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_VALHALL_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** Valhall based OpenCL GEMMReshaped configuration */
-class CLGEMMDefaultConfigReshapedValhall final : public ICLGEMMKernelConfiguration
+class ClGemmDefaultConfigReshapedValhall final : public IClGemmKernelConfig
 {
 public:
     /** Constructor
      *
      * @param[in] gpu GPU target
      */
-    CLGEMMDefaultConfigReshapedValhall(GPUTarget gpu);
+    ClGemmDefaultConfigReshapedValhall(GPUTarget gpu);
 
     // Inherited overridden method
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override;
@@ -50,6 +54,8 @@ private:
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDVALHALL_H */
+#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_VALHALL_H */
diff --git a/src/core/CL/gemm/reshaped/CLGEMMReshapedKernelConfiguration.h b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmReshapedKernelConfig.h
index de60698a91..c990c89a91 100644
--- a/src/core/CL/gemm/reshaped/CLGEMMReshapedKernelConfiguration.h
+++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmReshapedKernelConfig.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,21 +21,25 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMRESHAPEDKERNELCONFIGURATION_H
-#define ARM_COMPUTE_CLGEMMRESHAPEDKERNELCONFIGURATION_H
+#ifndef ARM_COMPUTE_CL_GEMM_RESHAPED_KERNEL_CONFIGURATION_H
+#define ARM_COMPUTE_CL_GEMM_RESHAPED_KERNEL_CONFIGURATION_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
-#include "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h"
-#include "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h"
 
 #include <memory>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** CLGEMMReshaped factory class */
-class CLGEMMReshapedKernelConfigurationFactory final
+class ClGemmReshapedKernelConfigurationFactory final
 {
 public:
     /** Static method to call the CLGEMMReshaped kernel configuration class accordingly with the GPU target
@@ -44,20 +48,22 @@ public:
      *
      * @return CLGEMMReshaped kernel configuration class
      */
-    static std::unique_ptr<ICLGEMMKernelConfiguration> create(GPUTarget gpu)
+    static std::unique_ptr<IClGemmKernelConfig> create(GPUTarget gpu)
     {
         switch(get_arch_from_target(gpu))
         {
             case GPUTarget::MIDGARD:
             case GPUTarget::BIFROST:
-                return std::make_unique<CLGEMMDefaultConfigReshapedBifrost>(gpu);
+                return std::make_unique<ClGemmDefaultConfigReshapedBifrost>(gpu);
             case GPUTarget::VALHALL:
-                return std::make_unique<CLGEMMDefaultConfigReshapedValhall>(gpu);
+                return std::make_unique<ClGemmDefaultConfigReshapedValhall>(gpu);
             default:
                 ARM_COMPUTE_ERROR("Not supported GPU target");
         }
     }
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMRESHAPEDKERNELCONFIGURATION_H */
+#endif /* ARM_COMPUTE_CL_GEMM_RESHAPED_KERNEL_CONFIGURATION_H */
diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.cpp
index 3645a0e141..7ed6b39f3e 100644
--- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
@@ -29,41 +29,45 @@
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <utility>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 using namespace arm_compute::misc::shape_calculator;
 
-CLGEMMDefaultConfigReshapedRHSOnlyBifrost::CLGEMMDefaultConfigReshapedRHSOnlyBifrost(GPUTarget gpu)
-    : ICLGEMMKernelConfiguration(gpu)
+ClGemmDefaultConfigReshapedRhsOnlyBifrost::ClGemmDefaultConfigReshapedRhsOnlyBifrost(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
 {
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
 {
-    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigReshapedRHSOnlyBifrost::*)(unsigned int m, unsigned int n, unsigned int k,
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedRhsOnlyBifrost::*)(unsigned int m, unsigned int n, unsigned int k,
                                              unsigned int b);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G51(&CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_f32,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_f16,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G51(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G52_f32,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G52_f16,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_f32,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_f16,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_f32,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_f16,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8);
 
     ConfigurationFunctionExecutorPtr func = nullptr;
 
@@ -87,7 +91,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     return (this->*func)(m, n, k, b);
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -109,7 +113,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -182,7 +186,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
     const float r_nk     = static_cast<float>(n) / static_cast<float>(k);
@@ -226,7 +230,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -243,7 +247,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -267,7 +271,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
     const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
@@ -354,7 +358,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
 
@@ -426,7 +430,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -443,7 +447,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -475,7 +479,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -491,7 +495,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -508,5 +512,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h
index db89d8317c..7b1a1fb04d 100644
--- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,24 +21,28 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYBIFROST_H
-#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYBIFROST_H
+#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_BIFROST_H
+#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_BIFROST_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** Bifrost based OpenCL GEMMReshapedOnlyRHS configuration */
-class CLGEMMDefaultConfigReshapedRHSOnlyBifrost final : public ICLGEMMKernelConfiguration
+class ClGemmDefaultConfigReshapedRhsOnlyBifrost final : public IClGemmKernelConfig
 {
 public:
     /** Constructor
      *
      * @param[in] gpu GPU target
      */
-    CLGEMMDefaultConfigReshapedRHSOnlyBifrost(GPUTarget gpu);
+    ClGemmDefaultConfigReshapedRhsOnlyBifrost(GPUTarget gpu);
 
     // Inherited overridden method
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override;
@@ -56,6 +60,8 @@ private:
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G51_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYBIFROST_H */
+#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_BIFROST_H */
diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.cpp
index a3f0509eda..4c6e633896 100644
--- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h"
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
@@ -29,33 +29,37 @@
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <utility>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 using namespace arm_compute::misc::shape_calculator;
 
-CLGEMMDefaultConfigReshapedRHSOnlyValhall::CLGEMMDefaultConfigReshapedRHSOnlyValhall(GPUTarget gpu)
-    : ICLGEMMKernelConfiguration(gpu)
+ClGemmDefaultConfigReshapedRhsOnlyValhall::ClGemmDefaultConfigReshapedRhsOnlyValhall(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
 {
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
 {
-    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigReshapedRHSOnlyValhall::*)(unsigned int m, unsigned int n, unsigned int k,
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedRhsOnlyValhall::*)(unsigned int m, unsigned int n, unsigned int k,
                                              unsigned int b);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_f32,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_f16,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8);
 
-    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G78_f32,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G78_f16,
-                                                                    &CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_u8);
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8);
 
     ConfigurationFunctionExecutorPtr func = nullptr;
 
@@ -74,7 +78,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     return (this->*func)(m, n, k, b);
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     if(m == 1)
     {
@@ -180,7 +184,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -230,7 +234,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     ARM_COMPUTE_UNUSED(k);
     ARM_COMPUTE_UNUSED(b);
@@ -254,7 +258,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
     const float r_mk     = static_cast<float>(m) / static_cast<float>(k);
@@ -397,7 +401,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
     }
 }
 
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
 {
     const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
     const float r_mk     = static_cast<float>(m) / static_cast<float>(k);
@@ -560,5 +564,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn
         }
     }
 }
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h
index a3b556c441..6a11ddb748 100644
--- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h
@@ -21,24 +21,28 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYVALHALL_H
-#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYVALHALL_H
+#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_VALHALL_H
+#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_VALHALL_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** Valhall based OpenCL GEMMReshapedOnlyRHS configuration */
-class CLGEMMDefaultConfigReshapedRHSOnlyValhall final : public ICLGEMMKernelConfiguration
+class ClGemmDefaultConfigReshapedRhsOnlyValhall final : public IClGemmKernelConfig
 {
 public:
     /** Constructor
      *
      * @param[in] gpu GPU target
      */
-    CLGEMMDefaultConfigReshapedRHSOnlyValhall(GPUTarget gpu);
+    ClGemmDefaultConfigReshapedRhsOnlyValhall(GPUTarget gpu);
 
     // Inherited overridden method
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override;
@@ -50,6 +54,8 @@ private:
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
     std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b);
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYVALHALL_H */
+#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_VALHALL_H */
diff --git a/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp
new file mode 100644
index 0000000000..7ed6b39f3e
--- /dev/null
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp
@@ -0,0 +1,518 @@
+/*
+ * Copyright (c) 2019-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/GPUTarget.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
+
+#include <utility>
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
+{
+using namespace arm_compute::misc::shape_calculator;
+
+ClGemmDefaultConfigReshapedRhsOnlyBifrost::ClGemmDefaultConfigReshapedRhsOnlyBifrost(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
+{
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+{
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedRhsOnlyBifrost::*)(unsigned int m, unsigned int n, unsigned int k,
+                                             unsigned int b);
+
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G51(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_u8);
+
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8);
+
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_u8);
+
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8);
+
+    ConfigurationFunctionExecutorPtr func = nullptr;
+
+    switch(_target)
+    {
+        case GPUTarget::G76:
+            func = configs_G76.get_function(data_type);
+            break;
+        case GPUTarget::G51:
+            func = configs_G51.get_function(data_type);
+            break;
+        case GPUTarget::G52:
+            func = configs_G52.get_function(data_type);
+            break;
+        default:
+            func = configs_G7x.get_function(data_type);
+            break;
+    }
+
+    ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not support for GEMM");
+    return (this->*func)(m, n, k, b);
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(m == 1)
+    {
+        if(n <= 2548)
+        {
+            return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 4, false, true, false, true, false);
+        }
+        else
+        {
+            return configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 8, false, true, false, true, false);
+        }
+    }
+    else
+    {
+        return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 4, false, true, false, true);
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    GEMMLHSMatrixInfo lhs_info_buf;
+    GEMMRHSMatrixInfo rhs_info_buf;
+    GEMMLHSMatrixInfo lhs_info_img;
+    GEMMRHSMatrixInfo rhs_info_img;
+
+    const bool is_workload_big = ((m * n * b) / 16) >= 2048;
+
+    if(m == 1)
+    {
+        if(n >= 8192)
+        {
+            const unsigned int h0 = std::max(n / 4, 1U);
+            return configure_lhs_rhs_info(m, n, 1, 4, 8, 1, h0, false, true, false, true, false);
+        }
+        else
+        {
+            const unsigned int h0 = std::max(n / 2, 1U);
+            if(n <= 204)
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, h0, false, true, false, true, false);
+            }
+            else
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, h0, false, true, false, true, false);
+            }
+        }
+    }
+    else
+    {
+        const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(16)), static_cast<int>(1));
+        if(is_workload_big)
+        {
+            std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, h0, false, true, false, true);
+        }
+        else
+        {
+            std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 2, 4, 8, 1, h0, false, true, false, true);
+        }
+    }
+
+    // Get lhs_info/rhs_info in case of OpenCL image
+    const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(16)), static_cast<int>(1));
+    if(is_workload_big)
+    {
+        std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, h0, false, true, false, false, true);
+    }
+    else
+    {
+        std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 2, 4, 8, 1, h0, false, true, false, true, true);
+    }
+
+    const TensorInfo  tensor_rhs_info(TensorShape(n, k, b), 1, DataType::F32);
+    const TensorShape shape = compute_rhs_reshaped_shape(tensor_rhs_info, rhs_info_img);
+    const TensorInfo  tensor_reshaped_info(shape, 1, DataType::F32);
+
+    // In case of vector by matrix or small workloads, we use the OpenCL buffer rather than the OpenCL image2d
+    const bool use_cl_image2d = ((m == 1) || ((((m * n * b) / 16) < 2048) && n < 128)) ? false : true;
+
+    if(bool(validate_image2d_support_on_rhs(tensor_reshaped_info, rhs_info_img)) && use_cl_image2d)
+    {
+        return std::make_pair(lhs_info_img, rhs_info_img);
+    }
+    else
+    {
+        return std::make_pair(lhs_info_buf, rhs_info_buf);
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
+    const float r_nk     = static_cast<float>(n) / static_cast<float>(k);
+
+    GEMMLHSMatrixInfo lhs_info_buf;
+    GEMMRHSMatrixInfo rhs_info_buf;
+    GEMMLHSMatrixInfo lhs_info_img;
+    GEMMRHSMatrixInfo rhs_info_img;
+
+    if(m == 1)
+    {
+        if(r_nk <= 0.4664f)
+        {
+            return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 16, false, true, false, true, false);
+        }
+        else
+        {
+            std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 8, 1, 16, false, true, false, true, true);
+            std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 1, 4, 8, 1, 16, false, true, false, true, false);
+
+            return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                       std::make_pair(lhs_info_buf, rhs_info_buf),
+                                       n, k, b, DataType::F32);
+        }
+    }
+    else
+    {
+        if(workload <= 274.4000f)
+        {
+            return configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 16, false, false, false, true, false);
+        }
+        else
+        {
+            std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, false, false, true, true);
+            std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, false, false, true, false);
+
+            return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                       std::make_pair(lhs_info_buf, rhs_info_buf),
+                                       n, k, b, DataType::F32);
+        }
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(m == 1)
+    {
+        const unsigned int n0 = n < 1280 ? 2 : 4;
+        const unsigned int h0 = std::max(n / n0, 1U);
+        return configure_lhs_rhs_info(m, n, 1, n0, 4, 1, h0, false, true, false, true);
+    }
+    else
+    {
+        return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, true, false, true);
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(m == 1)
+    {
+        if(n > 2048)
+        {
+            const unsigned int h0 = std::max(n / 4, 1U);
+            return configure_lhs_rhs_info(m, n, 1, 4, 4, 1, h0, false, true, false, true);
+        }
+        else
+        {
+            const unsigned int h0 = std::max(n / 2, 1U);
+            return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, h0, false, true, false, true);
+        }
+    }
+    else
+    {
+        return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 4, false, true, false, true);
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
+    const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
+    const float r_mk     = static_cast<float>(m) / static_cast<float>(k);
+    const float r_nk     = static_cast<float>(n) / static_cast<float>(k);
+
+    GEMMLHSMatrixInfo lhs_info_buf;
+    GEMMRHSMatrixInfo rhs_info_buf;
+    GEMMLHSMatrixInfo lhs_info_img;
+    GEMMRHSMatrixInfo rhs_info_img;
+
+    if(m == 1)
+    {
+        std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 16, false, true, false, false, false);
+
+        if(r_mk <= 0.0026f)
+        {
+            if(r_nk <= 0.4664f)
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 32, false, true, false, true, false);
+            }
+            else
+            {
+                std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 16, false, true, false, false, true);
+                return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                           std::make_pair(lhs_info_buf, rhs_info_buf),
+                                           n, k, b, DataType::F16);
+            }
+        }
+        else
+        {
+            if(r_mk <= 0.0148f)
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 32, false, true, false, true, false);
+            }
+            else
+            {
+                std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 16, false, true, false, false, true);
+                return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                           std::make_pair(lhs_info_buf, rhs_info_buf),
+                                           n, k, b, DataType::F16);
+            }
+        }
+    }
+    else
+    {
+        std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 5, 8, 4, 1, 2, false, false, false, false, false);
+
+        if(workload <= 362.6000f)
+        {
+            return configure_lhs_rhs_info(m, n, 2, 2, 8, 1, 16, false, false, false, true, false);
+        }
+        else
+        {
+            if(r_mn <= 22.6067f)
+            {
+                if(workload <= 708.8000f)
+                {
+                    std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 2, false, false, false, false, true);
+                    return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                               std::make_pair(lhs_info_buf, rhs_info_buf),
+                                               n, k, b, DataType::F16);
+                }
+                else
+                {
+                    return configure_lhs_rhs_info(m, n, 5, 8, 2, 1, 16, false, false, false, false, false);
+                }
+            }
+            else
+            {
+                if(r_nk <= 0.0917f)
+                {
+                    return configure_lhs_rhs_info(m, n, 2, 2, 8, 1, 16, false, false, false, true, false);
+                }
+                else
+                {
+                    std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 2, false, false, false, false, true);
+                    return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                               std::make_pair(lhs_info_buf, rhs_info_buf),
+                                               n, k, b, DataType::F16);
+                }
+            }
+        }
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+
+    if(m == 1)
+    {
+        return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 32, false, true, false, true, false);
+    }
+    else
+    {
+        const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
+        const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
+
+        if(workload <= 7449.60f)
+        {
+            if(workload <= 691.60f)
+            {
+                return configure_lhs_rhs_info(m, n, 2, 2, 8, 1, 8, false, false, false, false, false);
+            }
+            else
+            {
+                if(workload <= 4155.20f)
+                {
+                    return configure_lhs_rhs_info(m, n, 5, 2, 8, 1, 16, false, false, false, false, false);
+                }
+                else
+                {
+                    return configure_lhs_rhs_info(m, n, 5, 8, 2, 1, 32, false, false, false, false, false);
+                }
+            }
+        }
+        else
+        {
+            if(workload <= 16300.80f)
+            {
+                if(r_mn <= 44.56f)
+                {
+                    GEMMLHSMatrixInfo lhs_info_buf;
+                    GEMMRHSMatrixInfo rhs_info_buf;
+                    GEMMLHSMatrixInfo lhs_info_img;
+                    GEMMRHSMatrixInfo rhs_info_img;
+
+                    std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 8, 4, 4, 1, 1, false, true, false, false, true);
+                    std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 5, 2, 8, 1, 16, false, false, false, false, false);
+
+                    return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                               std::make_pair(lhs_info_buf, rhs_info_buf),
+                                               n, k, b, DataType::F16);
+                }
+                else
+                {
+                    return configure_lhs_rhs_info(m, n, 5, 2, 8, 1, 16, false, false, false, false, false);
+                }
+            }
+            else
+            {
+                GEMMLHSMatrixInfo lhs_info_buf;
+                GEMMRHSMatrixInfo rhs_info_buf;
+                GEMMLHSMatrixInfo lhs_info_img;
+                GEMMRHSMatrixInfo rhs_info_img;
+
+                std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 2, false, true, false, false, true);
+                std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 5, 2, 8, 1, 16, false, false, false, false, false);
+
+                return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                           std::make_pair(lhs_info_buf, rhs_info_buf),
+                                           n, k, b, DataType::F16);
+            }
+        }
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(m == 1)
+    {
+        const unsigned int n0 = n < 1280 ? 2 : 4;
+        const unsigned int h0 = std::max(n / n0, 1U);
+        return configure_lhs_rhs_info(m, n, 1, n0, 8, 1, h0, false, true, false, true);
+    }
+    else
+    {
+        return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, true, false, true);
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(dot8_supported(CLKernelLibrary::get().get_device()))
+    {
+        if(m == 1)
+        {
+            const unsigned int h0 = std::max(n / 2, 1U);
+            return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, h0, false, true, false, true);
+        }
+        else
+        {
+            const unsigned int h0 = std::max(n / 4, 1U);
+            return configure_lhs_rhs_info(m, n, 4, 4, 16, 1, h0, false, true, false, true);
+        }
+    }
+    else
+    {
+        const int h0 = std::max(std::min(static_cast<int>(n / 2), static_cast<int>(128)), static_cast<int>(1));
+        if(m == 1)
+        {
+            return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, h0, false, true, false, true);
+        }
+        else
+        {
+            return configure_lhs_rhs_info(m, n, 4, 2, 16, 1, h0, false, true, false, true);
+        }
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(m == 1)
+    {
+        const unsigned int h0 = std::max(n / 2, 1U);
+        return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, h0, false, true, false, true);
+    }
+    else
+    {
+        return configure_lhs_rhs_info(m, n, 4, 4, 16, 1, 2, false, true, false, true);
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(m == 1)
+    {
+        const unsigned int h0 = std::max(n / 2, 1U);
+        return configure_lhs_rhs_info(m, n, 1, 4, 16, 1, h0, false, true, false, true);
+    }
+    else
+    {
+        const unsigned int h0 = std::max(n / 2, 1U);
+        return configure_lhs_rhs_info(m, n, 4, 2, 16, 1, h0, false, true, false, true);
+    }
+}
+
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
diff --git a/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp
new file mode 100644
index 0000000000..4c6e633896
--- /dev/null
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp
@@ -0,0 +1,570 @@
+/*
+ * Copyright (c) 2020-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/GPUTarget.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
+
+#include <utility>
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
+{
+using namespace arm_compute::misc::shape_calculator;
+
+ClGemmDefaultConfigReshapedRhsOnlyValhall::ClGemmDefaultConfigReshapedRhsOnlyValhall(GPUTarget gpu)
+    : IClGemmKernelConfig(gpu)
+{
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type)
+{
+    using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedRhsOnlyValhall::*)(unsigned int m, unsigned int n, unsigned int k,
+                                             unsigned int b);
+
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8);
+
+    CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f32,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f16,
+                                                                    &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8);
+
+    ConfigurationFunctionExecutorPtr func = nullptr;
+
+    switch(_target)
+    {
+        case GPUTarget::G78:
+            func = configs_G78.get_function(data_type);
+            break;
+        case GPUTarget::G77:
+        default:
+            func = configs_G77.get_function(data_type);
+            break;
+    }
+
+    ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not support for GEMM");
+    return (this->*func)(m, n, k, b);
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    if(m == 1)
+    {
+        const float r_mn = static_cast<float>(m) / static_cast<float>(n);
+        const float r_mk = static_cast<float>(m) / static_cast<float>(k);
+
+        if(r_mk <= 0.0064484127797186375)
+        {
+            if(r_mn <= 0.0028273810748942196)
+            {
+                GEMMLHSMatrixInfo lhs_info_buf;
+                GEMMRHSMatrixInfo rhs_info_buf;
+                GEMMLHSMatrixInfo lhs_info_img;
+                GEMMRHSMatrixInfo rhs_info_img;
+
+                const unsigned int h0 = std::max(n / 4, 1U);
+                std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 8, 1, 16, 0, 1, 0, 0, 1);
+                std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 1, 4, 4, 1, h0, 0, 1, 0, 1, 0);
+
+                return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                           std::make_pair(lhs_info_buf, rhs_info_buf),
+                                           n, k, b, DataType::F32);
+            }
+            else
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 8, 0, 1, 0, 0, 0);
+            }
+        }
+        else
+        {
+            if(r_mk <= 0.020312500186264515)
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 4, 0, 1, 0, 0, 0);
+            }
+            else
+            {
+                return configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 16, 0, 1, 0, 1, 0);
+            }
+        }
+    }
+    else
+    {
+        const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
+        const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
+        const float r_mk     = static_cast<float>(m) / static_cast<float>(k);
+
+        if(workload <= 1999.2000122070312)
+        {
+            if(workload <= 747.1999816894531)
+            {
+                return configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 8, 0, 1, 0, 1, 0);
+            }
+            else
+            {
+                GEMMLHSMatrixInfo lhs_info_buf;
+                GEMMRHSMatrixInfo rhs_info_buf;
+                GEMMLHSMatrixInfo lhs_info_img;
+                GEMMRHSMatrixInfo rhs_info_img;
+                std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 2, 0, 0, 0, 1, 1);
+                std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 8, 0, 1, 0, 1, 0);
+
+                return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                           std::make_pair(lhs_info_buf, rhs_info_buf),
+                                           n, k, b, DataType::F32);
+            }
+        }
+        else
+        {
+            if(r_mn <= 0.03348214365541935)
+            {
+                if(r_mk <= 0.028125000186264515)
+                {
+                    return configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 8, 0, 1, 0, 1, 0);
+                }
+                else
+                {
+                    GEMMLHSMatrixInfo lhs_info_buf;
+                    GEMMRHSMatrixInfo rhs_info_buf;
+                    GEMMLHSMatrixInfo lhs_info_img;
+                    GEMMRHSMatrixInfo rhs_info_img;
+                    std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 2, 0, 0, 0, 1, 1);
+                    std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 8, 0, 1, 0, 1, 0);
+
+                    return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                               std::make_pair(lhs_info_buf, rhs_info_buf),
+                                               n, k, b, DataType::F32);
+                }
+            }
+            else
+            {
+                GEMMLHSMatrixInfo lhs_info_buf;
+                GEMMRHSMatrixInfo rhs_info_buf;
+                GEMMLHSMatrixInfo lhs_info_img;
+                GEMMRHSMatrixInfo rhs_info_img;
+                std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, 0, 1, 0, 0, 1);
+                std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 16, 0, 1, 0, 1, 0);
+
+                return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img),
+                                           std::make_pair(lhs_info_buf, rhs_info_buf),
+                                           n, k, b, DataType::F32);
+            }
+        }
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(m == 1)
+    {
+        const unsigned int h0 = std::max(n / 2, 1U);
+        if(n <= 836.0)
+        {
+            return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, h0, 0, 1, 0, 1, 0);
+        }
+        else
+        {
+            return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, h0, 0, 1, 0, 1, 0);
+        }
+    }
+    else if(m < 128)
+    {
+        const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(256)), static_cast<int>(1));
+        if(k >= 512)
+        {
+            return configure_lhs_rhs_info(m, n, 2, 4, 16, 1, h0, 0, 1, 0, 0);
+        }
+        else
+        {
+            return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, h0, 0, 1, 0, 0);
+        }
+    }
+    else
+    {
+        const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(256)), static_cast<int>(1));
+        if(n >= 64)
+        {
+            return configure_lhs_rhs_info(m, n, 4, 8, 4, 1, h0, 0, 1, 0, 0);
+        }
+        else
+        {
+            if(k >= 512)
+            {
+                return configure_lhs_rhs_info(m, n, 2, 4, 16, 1, h0, 0, 1, 0, 0);
+            }
+            else
+            {
+                return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, h0, 0, 1, 0, 0);
+            }
+        }
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    ARM_COMPUTE_UNUSED(k);
+    ARM_COMPUTE_UNUSED(b);
+
+    if(m == 1)
+    {
+        const unsigned int h0 = std::max(n / 2, 1U);
+        return configure_lhs_rhs_info(m, n, 1, 4, 16, 1, h0, 0, 1, 0, 1);
+    }
+    else
+    {
+        const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(256)), static_cast<int>(1));
+        if(m >= 28)
+        {
+            return configure_lhs_rhs_info(m, n, 4, 4, 16, 1, h0, 0, 1, 0, 1);
+        }
+        else
+        {
+            return configure_lhs_rhs_info(m, n, 2, 4, 16, 1, h0, 0, 1, 0, 1);
+        }
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
+    const float r_mk     = static_cast<float>(m) / static_cast<float>(k);
+    const float r_nk     = static_cast<float>(n) / static_cast<float>(k);
+    const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
+
+    if(m == 1)
+    {
+        if(workload <= 278.7000f)
+        {
+            if(workload <= 7.5000f)
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0);
+            }
+            else
+            {
+                if(r_mn <= 0.0031f)
+                {
+                    if(workload <= 256.6000f)
+                    {
+                        if(workload <= 16.7500f)
+                        {
+                            if(r_nk <= 1.6671f)
+                            {
+                                return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0);
+                            }
+                            else
+                            {
+                                return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0);
+                            }
+                        }
+                        else
+                        {
+                            return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0);
+                        }
+                    }
+                    else
+                    {
+                        return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0);
+                    }
+                }
+                else
+                {
+                    if(r_mk <= 0.0027f)
+                    {
+                        if(r_mk <= 0.0014f)
+                        {
+                            return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0);
+                        }
+                        else
+                        {
+                            if(workload <= 8.9500f)
+                            {
+                                return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0);
+                            }
+                            else
+                            {
+                                return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0);
+                            }
+                        }
+                    }
+                    else
+                    {
+                        if(workload <= 14.1500f)
+                        {
+                            return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0);
+                        }
+                        else
+                        {
+                            if(r_mk <= 0.0041f)
+                            {
+                                return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0);
+                            }
+                            else
+                            {
+                                return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0);
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        else
+        {
+            if(workload <= 363.7000f)
+            {
+                if(r_mk <= 0.0031f)
+                {
+                    return configure_lhs_rhs_info(m, n, 1, 4, 2, 1, 32, 0, 1, 0, 1, 0);
+                }
+                else
+                {
+                    return configure_lhs_rhs_info(m, n, 1, 4, 4, 1, 32, 0, 1, 0, 1, 0);
+                }
+            }
+            else
+            {
+                return configure_lhs_rhs_info(m, n, 1, 4, 2, 1, 32, 0, 1, 0, 1, 0);
+            }
+        }
+    }
+    else
+    {
+        if(workload <= 1384.8000f)
+        {
+            if(workload <= 704.0000f)
+            {
+                return configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 32, 0, 1, 0, 1, 0);
+            }
+            else
+            {
+                return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 4, 0, 0, 0, 1, 1);
+            }
+        }
+        else
+        {
+            if(workload <= 16761.6006f)
+            {
+                if(r_mn <= 187.1250f)
+                {
+                    return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 16, 0, 0, 0, 1, 1);
+                }
+                else
+                {
+                    return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 4, 0, 0, 0, 1, 1);
+                }
+            }
+            else
+            {
+                if(r_mk <= 432.4630f)
+                {
+                    return configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 16, 0, 0, 0, 1, 1);
+                }
+                else
+                {
+                    return configure_lhs_rhs_info(m, n, 2, 4, 4, 1, 16, 0, 1, 0, 1, 1);
+                }
+            }
+        }
+    }
+}
+
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b)
+{
+    const float r_mn     = static_cast<float>(m) / static_cast<float>(n);
+    const float r_mk     = static_cast<float>(m) / static_cast<float>(k);
+    const float r_nk     = static_cast<float>(n) / static_cast<float>(k);
+    const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f;
+
+    if(m == 1)
+    {
+        if(r_mn <= 0.0038f)
+        {
+            if(workload <= 353.9000f)
+            {
+                if(workload <= 278.7000f)
+                {
+                    return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0);
+                }
+                else
+                {
+                    if(r_mk <= 0.0004f)
+                    {
+                        return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0);
+                    }
+                    else
+                    {
+                        if(r_mk <= 0.0030f)
+                        {
+                            return configure_lhs_rhs_info(m, n, 1, 8, 4, 1, 8, 0, 1, 1, 0, 1);
+                        }
+                        else
+                        {
+                            return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0);
+                        }
+                    }
+                }
+            }
+            else
+            {
+                if(r_nk <= 1.9384f)
+                {
+                    return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0);
+                }
+                else
+                {
+                    return configure_lhs_rhs_info(m, n, 1, 8, 4, 1, 8, 0, 1, 1, 0, 1);
+                }
+            }
+        }
+        else
+        {
+            if(r_nk <= 1.0368f)
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 32, 0, 0, 1, 0, 0);
+            }
+            else
+            {
+                return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0);
+            }
+        }
+    }
+    else
+    {
+        if(workload <= 1422.4000f)
+        {
+            if(workload <= 704.0000f)
+            {
+                return configure_lhs_rhs_info(m, n, 2, 2, 8, 1, 32, 0, 0, 1, 0, 0);
+            }
+            else
+            {
+                if(workload <= 1197.6000f)
+                {
+                    return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 8, 0, 1, 1, 0, 1);
+                }
+                else
+                {
+                    if(workload <= 1241.6000f)
+                    {
+                        return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0);
+                    }
+                    else
+                    {
+                        return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 8, 0, 1, 1, 0, 1);
+                    }
+                }
+            }
+        }
+        else
+        {
+            if(workload <= 2769.6000f)
+            {
+                if(workload <= 1846.4000f)
+                {
+                    if(r_mn <= 2.4927f)
+                    {
+                        return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0);
+                    }
+                    else
+                    {
+                        return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0);
+                    }
+                }
+                else
+                {
+                    if(r_mn <= 0.6261f)
+                    {
+                        return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0);
+                    }
+                    else
+                    {
+                        if(r_mk <= 3.4453f)
+                        {
+                            if(r_mn <= 1.4135f)
+                            {
+                                return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0);
+                            }
+                            else
+                            {
+                                return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0);
+                            }
+                        }
+                        else
+                        {
+                            return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0);
+                        }
+                    }
+                }
+            }
+            else
+            {
+                if(r_nk <= 0.0302f)
+                {
+                    return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 8, 0, 1, 1, 0, 1);
+                }
+                else
+                {
+                    if(r_mk <= 181.3750f)
+                    {
+                        return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0);
+                    }
+                    else
+                    {
+                        if(workload <= 28035.2002f)
+                        {
+                            return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0);
+                        }
+                        else
+                        {
+                            if(r_mk <= 808.6667f)
+                            {
+                                return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0);
+                            }
+                            else
+                            {
+                                return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0);
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfiguration.h b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmReshapedOnlyRhsKernelConfig.h
index 001b98dca8..8fd71276a0 100644
--- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfiguration.h
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmReshapedOnlyRhsKernelConfig.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,21 +21,25 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ARM_COMPUTE_CLGEMMRESHAPEDONLYRHSKERNELCONFIGURATION_H
-#define ARM_COMPUTE_CLGEMMRESHAPEDONLYRHSKERNELCONFIGURATION_H
+#ifndef ARM_COMPUTE_CL_GEMM_RESHAPED_ONLY_RHS_KERNEL_CONFIGURATION_H
+#define ARM_COMPUTE_CL_GEMM_RESHAPED_ONLY_RHS_KERNEL_CONFIGURATION_H
 
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
-#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h"
-#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h"
 
 #include <memory>
 
 namespace arm_compute
 {
-namespace cl_gemm
+namespace opencl
+{
+namespace kernels
+{
+namespace gemm
 {
 /** CLGEMMReshapedOnlyRHS factory class */
-class CLGEMMReshapedOnlyRHSKernelConfigurationFactory final
+class ClGemmReshapedOnlyRhsKernelConfigurationFactory final
 {
 public:
     /** Static method to call the CLGEMMReshapedOnlyRHS kernel configuration class accordingly with the GPU target
@@ -44,20 +48,22 @@ public:
      *
      * @return CLGEMMReshapedOnlyRHS kernel configuration class
      */
-    static std::unique_ptr<ICLGEMMKernelConfiguration> create(GPUTarget gpu)
+    static std::unique_ptr<IClGemmKernelConfig> create(GPUTarget gpu)
     {
         switch(get_arch_from_target(gpu))
         {
             case GPUTarget::MIDGARD:
             case GPUTarget::BIFROST:
-                return std::make_unique<CLGEMMDefaultConfigReshapedRHSOnlyBifrost>(gpu);
+                return std::make_unique<ClGemmDefaultConfigReshapedRhsOnlyBifrost>(gpu);
             case GPUTarget::VALHALL:
-                return std::make_unique<CLGEMMDefaultConfigReshapedRHSOnlyValhall>(gpu);
+                return std::make_unique<ClGemmDefaultConfigReshapedRhsOnlyValhall>(gpu);
             default:
                 ARM_COMPUTE_ERROR("Not supported GPU target");
         }
     }
 };
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
 } // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMRESHAPEDONLYRHSKERNELCONFIGURATION_H */
+#endif /* ARM_COMPUTE_CL_GEMM_RESHAPED_ONLY_RHS_KERNEL_CONFIGURATION_H */
diff --git a/src/core/helpers/MemoryHelpers.h b/src/core/helpers/MemoryHelpers.h
new file mode 100644
index 0000000000..6756a90c25
--- /dev/null
+++ b/src/core/helpers/MemoryHelpers.h
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef SRC_COMMON_MEMORY_HELPERS_H
+#define SRC_COMMON_MEMORY_HELPERS_H
+
+#include "arm_compute/core/ITensorPack.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/experimental/Types.h"
+#include "arm_compute/runtime/MemoryGroup.h"
+
+#include <memory>
+#include <utility>
+#include <vector>
+
+namespace arm_compute
+{
+inline int offset_int_vec(int offset)
+{
+    return ACL_INT_VEC + offset;
+}
+
+template <typename TensorType>
+using WorkspaceData = std::vector<std::pair<int, std::unique_ptr<TensorType>>>;
+
+template <typename TensorType>
+WorkspaceData<TensorType> manage_workspace(const experimental::MemoryRequirements &mem_reqs,
+                                           MemoryGroup                            &mgroup,
+                                           ITensorPack &run_pack, ITensorPack &prep_pack)
+{
+    WorkspaceData<TensorType> workspace_memory;
+    for(const auto &req : mem_reqs)
+    {
+        if(req.size == 0)
+        {
+            continue;
+        }
+
+        const auto aux_info = TensorInfo{ TensorShape(req.size), 1, DataType::U8 };
+        workspace_memory.emplace_back(req.slot, std::make_unique<TensorType>());
+
+        auto aux_tensor = workspace_memory.back().second.get();
+        ARM_COMPUTE_ERROR_ON_NULLPTR(aux_tensor);
+        aux_tensor->allocator()->init(aux_info);
+
+        if(req.lifetime == experimental::MemoryLifetime::Temporary)
+        {
+            mgroup.manage(aux_tensor);
+        }
+        else
+        {
+            prep_pack.add_tensor(req.slot, aux_tensor);
+        }
+        run_pack.add_tensor(req.slot, aux_tensor);
+    }
+
+    for(auto &mem : workspace_memory)
+    {
+        auto tensor = mem.second.get();
+        tensor->allocator()->allocate();
+    }
+
+    return workspace_memory;
+}
+} // namespace arm_compute
+#endif /* SRC_COMMON_MEMORY_HELPERS_H */
diff --git a/src/graph/backends/CL/CLNodeValidator.cpp b/src/graph/backends/CL/CLNodeValidator.cpp
index 1136086375..312cda399f 100644
--- a/src/graph/backends/CL/CLNodeValidator.cpp
+++ b/src/graph/backends/CL/CLNodeValidator.cpp
@@ -30,12 +30,6 @@
 #include "arm_compute/runtime/CPP/CPPFunctions.h"
 #include "src/core/CL/kernels/CLDepthConvertLayerKernel.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/CL/kernels/CLIm2ColKernel.h"
 #include "src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.h"
 #include "src/core/CL/kernels/CLWeightsReshapeKernel.h"
diff --git a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
index 00d9a9ec89..8d1a91e420 100644
--- a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
@@ -31,7 +31,6 @@
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "src/core/CL/kernels/CLDeconvolutionLayerUpsampleKernel.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/CL/kernels/CLWeightsReshapeKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"
 
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 945675f4dd..991472bb7a 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -35,11 +35,6 @@
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/gpu/cl/kernels/ClTransposeKernel.h"
 #include "support/Cast.h"
 
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index cf1a82bc5a..1bc785a0a7 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -23,646 +23,48 @@
  */
 #include "arm_compute/runtime/CL/functions/CLGEMM.h"
 
+#include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/GPUTarget.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/KernelDescriptors.h"
-#include "arm_compute/core/Log.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "arm_compute/runtime/ITensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/utils/helpers/float_ops.h"
-#include "src/runtime/CL/gemm/CLGEMMKernelSelection.h"
-#include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h"
-#include "support/Cast.h"
-#include "utils/TypePrinter.h"
+#include "arm_compute/runtime/CL/functions/CLGEMM.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/runtime/gpu/cl/operators/ClGemm.h"
 
 namespace arm_compute
 {
-using namespace arm_compute::misc::shape_calculator;
-using namespace arm_compute::cl_gemm;
-using namespace arm_compute::utils::cast;
-
-namespace weights_transformations
-{
-CLGEMMReshapeRHSMatrixKernelManaged::CLGEMMReshapeRHSMatrixKernelManaged()
-    : _kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>())
-{
-}
-
-CLGEMMReshapeRHSMatrixKernelManaged::~CLGEMMReshapeRHSMatrixKernelManaged() = default;
-
-void CLGEMMReshapeRHSMatrixKernelManaged::run()
-{
-    _output.allocator()->allocate();
-    CLScheduler::get().enqueue(*_kernel, false);
-    _reshape_run = true;
-}
-
-void CLGEMMReshapeRHSMatrixKernelManaged::release()
-{
-    _output.allocator()->free();
-}
-
-ICLTensor *CLGEMMReshapeRHSMatrixKernelManaged::get_weights()
-{
-    return &_output;
-}
-
-uint32_t CLGEMMReshapeRHSMatrixKernelManaged::uid()
-{
-    return _uid;
-}
-
-void CLGEMMReshapeRHSMatrixKernelManaged::configure(const ICLTensor *input, GEMMRHSMatrixInfo info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input, info);
-}
-
-void CLGEMMReshapeRHSMatrixKernelManaged::configure(const CLCompileContext &compile_context, const ICLTensor *input, GEMMRHSMatrixInfo info)
-{
-    _kernel->configure(compile_context, input, &_output, info);
-}
-} // namespace weights_transformations
-
-namespace
-{
-inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type)
-{
-    switch(kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE_V1:
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        case CLGEMMKernelType::RESHAPED_V1:
-        case CLGEMMKernelType::RESHAPED:
-        {
-            return true;
-        }
-        default:
-        {
-            return false;
-        }
-    }
-}
-//Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type
-inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run)
-{
-    auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run);
-    if(bool(gemm_kernel))
-    {
-        if(validate_gemm_kernel(gemm_kernel.gemm_type))
-        {
-            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str());
-            return gemm_kernel.gemm_type;
-        }
-    }
-    gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run);
-    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str());
-    return gemm_kernel.gemm_type;
-}
-// Validate lhs_info and rhs_info for reshaped only rhs kernel
-inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c,
-                                                    const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info)
-{
-    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel
-    TensorInfo tmp_b_info{};
-    // Validate reshape RHS kernel
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    if(!bool(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info)))
-    {
-        return false;
-    }
-    // Validate mm kernel
-    gemm_kernel_info.lhs_info  = lhs_info;
-    gemm_kernel_info.rhs_info  = rhs_info;
-    gemm_kernel_info.has_pad_y = false;
-    if(!bool(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
-    {
-        return false;
-    }
-    gemm_kernel_info.has_pad_y = true;
-    if(!bool(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
-    {
-        return false;
-    }
-    return true;
-}
-
-//Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs
-inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a,
-                                                                                                 const ITensorInfo *b,
-                                                                                                 const ITensorInfo *c, const ITensorInfo *output)
-{
-    auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query);
-    if(config)
-    {
-        if(validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info))
-        {
-            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
-            return { config.lhs_info, config.rhs_info };
-        }
-    }
-    config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query);
-    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
-    return { config.lhs_info, config.rhs_info };
-}
-
-// Validate lhs_info and rhs_info for reshaped kernel
-inline bool validate_lhs_rhs_info_reshaped(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c,
-                                           const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info, bool reinterpret_input_as_3d)
-{
-    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped kernel
-    TensorInfo tmp_a_info{};
-    TensorInfo tmp_b_info{};
-
-    // Validate reshape LHS kernel
-    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, reinterpret_input_as_3d)));
-    if(!bool(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, reinterpret_input_as_3d)))
-    {
-        return false;
-    }
-
-    // Validate reshape RHS kernel
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    if(!bool(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info)))
-    {
-        return false;
-    }
-    // Validate mm kernel
-    gemm_kernel_info.lhs_info = lhs_info;
-    gemm_kernel_info.rhs_info = rhs_info;
-    if(!bool(CLGEMMMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
-    {
-        return false;
-    }
-    return true;
-}
-
-//Automatically select between mlgo (prioritized) and default heuristics for reshaped kernel configs
-inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, const ITensorInfo *b,
-                                                                                        const ITensorInfo *c, const ITensorInfo *output, bool reinterpret_input_as_3d)
-{
-    auto config = auto_heuristics::select_mlgo_gemm_config_reshaped(query);
-    if(config)
-    {
-        if(validate_lhs_rhs_info_reshaped(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info, reinterpret_input_as_3d))
-        {
-            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
-            return { config.lhs_info, config.rhs_info };
-        }
-    }
-    config = auto_heuristics::select_default_gemm_config_reshaped(query);
-    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
-    return { config.lhs_info, config.rhs_info };
-}
-
-} // namespace
+using namespace arm_compute::experimental;
+using OperatorType = opencl::ClGemm;
+
+struct CLGEMM::Impl
+{
+    const ICLTensor              *a{ nullptr };
+    const ICLTensor              *b{ nullptr };
+    const ICLTensor              *c{ nullptr };
+    ICLTensor                    *dst{ nullptr };
+    std::unique_ptr<OperatorType> op{ nullptr };
+    MemoryGroup                   memory_group{};
+    IWeightsManager              *weights_manager{ nullptr };
+    CLTensor                      weights_transformed{};
+    ITensorPack                   run_pack{};
+    ITensorPack                   prep_pack{};
+    MemoryRequirements            aux_mem_req{};
+    WorkspaceData<CLTensor>       workspace_tensors{};
+    bool                          _is_prepared{ false };
+};
 
 CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
-    : _memory_group(std::move(memory_manager)),
-      _weights_manager(weights_manager),
-      _mm_kernel(std::make_unique<CLGEMMMatrixMultiplyKernel>()),
-      _reshape_lhs_kernel(std::make_unique<CLGEMMReshapeLHSMatrixKernel>()),
-      _reshape_rhs_kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>()),
-      _reshape_rhs_kernel_managed(std::make_unique<weights_transformations::CLGEMMReshapeRHSMatrixKernelManaged>()),
-      _mm_reshaped_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedKernel>()),
-      _mm_reshaped_only_rhs_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>()),
-      _mm_reshaped_only_rhs_fallback_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>()),
-      _tmp_a(),
-      _tmp_b(),
-      _original_b(nullptr),
-      _lhs(nullptr),
-      _dst(nullptr),
-      _reshape_b_only_on_first_run(false),
-      _is_prepared(false),
-      _gemm_kernel_type(CLGEMMKernelType::NATIVE_V1)
+    : _impl(std::make_unique<Impl>())
 {
+    _impl->memory_group    = MemoryGroup(memory_manager);
+    _impl->weights_manager = weights_manager;
 }
 
 CLGEMM::~CLGEMM() = default;
 
-void CLGEMM::configure_native_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                 const GEMMInfo &gemm_info)
-{
-    const unsigned int m          = gemm_info.reinterpret_input_as_3d() ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
-    const unsigned int n          = b->info()->dimension(0);
-    const unsigned int k          = a->info()->dimension(0);
-    const GPUTarget    gpu_target = CLScheduler::get().target();
-
-    // Set the target for the kernels
-    _mm_kernel->set_target(gpu_target);
-
-    GEMMReshapeInfo reshape_info(m, n, k, 1, 1, gemm_info.depth_output_gemm3d(), gemm_info.reinterpret_input_as_3d(), gemm_info.broadcast_bias());
-
-    // Configure and tune matrix multiply kernel
-    _mm_kernel->configure(compile_context, a, b, c, output, alpha, beta, false, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info());
-
-    // Tune kernel statically
-    CLScheduler::get().tune_kernel_static(*_mm_kernel);
-}
-
-void CLGEMM::configure_reshaped_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                   const GEMMInfo &gemm_info)
-{
-    bool               reinterpret_input_as_3d   = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                         = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
-    const unsigned int n                         = b->info()->dimension(0);
-    const unsigned int k                         = a->info()->dimension(0);
-    const int          depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
-    const GPUTarget    gpu_target                = CLScheduler::get().target();
-    int                mult_transpose1xW_width   = 1;
-    int                mult_interleave4x4_height = 1;
-
-    // Set the target for the kernels
-    _reshape_lhs_kernel->set_target(gpu_target);
-    _mm_kernel->set_target(gpu_target);
-
-    if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
-    {
-        mult_transpose1xW_width   = 4;
-        mult_interleave4x4_height = 2;
-    }
-
-    GEMMRHSMatrixInfo rhs_info;
-    rhs_info.n0         = 16 / b->info()->element_size();
-    rhs_info.k0         = 1;
-    rhs_info.h0         = mult_transpose1xW_width;
-    rhs_info.interleave = false;
-    rhs_info.transpose  = false;
-
-    GEMMLHSMatrixInfo lhs_info;
-    lhs_info.m0         = 4;
-    lhs_info.k0         = 4;
-    lhs_info.v0         = mult_interleave4x4_height;
-    lhs_info.interleave = true;
-    lhs_info.transpose  = true;
-
-    GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias());
-
-    const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b));
-
-    // Manage intermediate buffers
-    _memory_group.manage(&_tmp_a);
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _memory_group.manage(&_tmp_b);
-    }
-
-    // Configure interleave kernel
-    _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, reinterpret_input_as_3d);
-
-    // Configure transpose kernel
-    ICLTensor *reshaped_rhs = &_tmp_b;
-    if(_weights_manager && _weights_manager->are_weights_managed(b))
-    {
-        _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info);
-        reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get()));
-    }
-    else
-    {
-        _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
-    }
-
-    // Configure and tune matrix multiply kernel
-    _mm_kernel->configure(compile_context, &_tmp_a, reshaped_rhs, c, output, alpha, beta, true, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info());
-
-    CLScheduler::get().tune_kernel_static(*_mm_kernel);
-
-    // Allocate intermediate tensors
-    _tmp_a.allocator()->allocate();
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _tmp_b.allocator()->allocate();
-    }
-}
-
-void CLGEMM::configure_reshaped_v2(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                   const GEMMInfo &gemm_info)
-{
-    DataType           data_type               = a->info()->data_type();
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
-    const unsigned int n                       = b->info()->dimension(0);
-    const unsigned int k                       = a->info()->dimension(0);
-    const unsigned int batch_size              = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2);
-    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-    const GPUTarget    gpu_target              = CLScheduler::get().target();
-    bool               broadcast_bias          = gemm_info.broadcast_bias();
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m                       = m;
-    kernel_info.n                       = n;
-    kernel_info.k                       = k;
-    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    kernel_info.reinterpret_input_as_3d = false;
-    kernel_info.broadcast_bias          = broadcast_bias;
-    kernel_info.activation_info         = gemm_info.activation_info();
-
-    // Set the target for the kernels
-    _reshape_lhs_kernel->set_target(gpu_target);
-    _mm_kernel->set_target(gpu_target);
-
-    const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b));
-
-    // Manage intermediate buffers
-    _memory_group.manage(&_tmp_a);
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _memory_group.manage(&_tmp_b);
-    }
-
-    // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel
-
-    GEMMLHSMatrixInfo lhs_info{};
-    GEMMRHSMatrixInfo rhs_info{};
-
-    // Pick up the GEMM configuration
-    std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a->info(), b->info(),
-                                                                    c == nullptr ? nullptr : c->info(), output->info(), gemm_info.reinterpret_input_as_3d());
-
-    _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d());
-
-    ICLTensor *reshaped_rhs = &_tmp_b;
-    if(_weights_manager && _weights_manager->are_weights_managed(b))
-    {
-        _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info);
-        reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get()));
-    }
-    else
-    {
-        _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
-    }
-
-    // Configure and tune matrix multiply kernel
-    _mm_reshaped_kernel->configure(compile_context, &_tmp_a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
-
-    // Allocate intermediate tensors
-    _tmp_a.allocator()->allocate();
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _tmp_b.allocator()->allocate();
-    }
-}
-
-void CLGEMM::configure_reshaped_only_rhs(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                         const GEMMInfo &gemm_info)
-{
-    DataType           data_type               = a->info()->data_type();
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
-    const unsigned int n                       = b->info()->dimension(0);
-    const unsigned int k                       = a->info()->dimension(0);
-    const unsigned int batch_size              = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2);
-    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-    const GPUTarget    gpu_target              = CLScheduler::get().target();
-    bool               broadcast_bias          = gemm_info.broadcast_bias();
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m                       = m;
-    kernel_info.n                       = n;
-    kernel_info.k                       = k;
-    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
-    kernel_info.broadcast_bias          = broadcast_bias;
-    kernel_info.activation_info         = gemm_info.activation_info();
-
-    // Set the target for the kernels
-    _mm_kernel->set_target(gpu_target);
-
-    const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b));
-
-    // Manage intermediate buffers
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _memory_group.manage(&_tmp_b);
-    }
-
-    GEMMLHSMatrixInfo lhs_info{};
-    GEMMRHSMatrixInfo rhs_info{};
-
-    // Pick up the GEMM configuration
-    std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a->info(), b->info(),
-                                                                             c == nullptr ? nullptr : c->info(), output->info());
-
-    ICLTensor *reshaped_rhs = &_tmp_b;
-    if(_weights_manager && _weights_manager->are_weights_managed(b))
-    {
-        _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info);
-        reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get()));
-    }
-    else
-    {
-        _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
-    }
-
-    // Configure two variants of CLGEMMMatrixMultiplyReshapedOnlyRHSKernel (has_pad_y = false/true)
-    // During the prepare stage we check the padding requirement for the lhs and dst tensors. If they do not have
-    // pad y, we dispatch CLGEMMMatrixMultiplyReshapedOnlyRHSKernel with has_pad_y = false
-
-    // Configure matrix multiply kernel with no y padding support
-    kernel_info.has_pad_y = false;
-    _mm_reshaped_only_rhs_kernel->configure(compile_context, a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
-
-    // Configure matrix multiply kernel with y padding support
-    kernel_info.has_pad_y = true;
-    _mm_reshaped_only_rhs_fallback_kernel->configure(compile_context, a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _tmp_b.allocator()->allocate();
-    }
-}
-
-Status CLGEMM::validate_native_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(output);
-
-    // Get the GPU target
-    const GPUTarget    gpu_target              = CLScheduler::get().target();
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
-    const unsigned int n                       = b->dimension(0);
-    const unsigned int k                       = a->dimension(0);
-    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-
-    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d, gemm_info.broadcast_bias());
-
-    // Validate matrix multiply
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(a, b, c, output, alpha, beta,
-                                                                     false, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info()));
-
-    return Status{};
-}
-
-Status CLGEMM::validate_reshaped_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(output);
-
-    TensorInfo tmp_a_info{};
-    TensorInfo tmp_b_info{};
-
-    // Get the GPU target
-    const GPUTarget    gpu_target                = CLScheduler::get().target();
-    const unsigned int m                         = gemm_info.reinterpret_input_as_3d() ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
-    const unsigned int n                         = b->dimension(0);
-    const unsigned int k                         = a->dimension(0);
-    int                mult_transpose1xW_width   = 1;
-    int                mult_interleave4x4_height = 1;
-    const int          depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
-
-    if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
-    {
-        mult_transpose1xW_width   = 4;
-        mult_interleave4x4_height = 2;
-    }
-
-    GEMMRHSMatrixInfo rhs_info;
-    rhs_info.n0         = 16 / b->element_size();
-    rhs_info.k0         = 1;
-    rhs_info.h0         = mult_transpose1xW_width;
-    rhs_info.interleave = false;
-    rhs_info.transpose  = false;
-
-    GEMMLHSMatrixInfo lhs_info;
-    lhs_info.m0         = 4;
-    lhs_info.k0         = 4;
-    lhs_info.v0         = mult_interleave4x4_height;
-    lhs_info.interleave = true;
-    lhs_info.transpose  = true;
-
-    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias());
-
-    // Validate interleave kernel
-    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
-
-    // Validate transpose kernel
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
-
-    // Validate matrix multiply
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta,
-                                                                     true, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info()));
-
-    return Status{};
-}
-
-Status CLGEMM::validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(output);
-
-    TensorInfo tmp_a_info{};
-    TensorInfo tmp_b_info{};
-
-    // Get the GPU target
-    const GPUTarget    gpu_target              = CLScheduler::get().target();
-    DataType           data_type               = a->data_type();
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
-    const unsigned int n                       = b->dimension(0);
-    const unsigned int k                       = a->dimension(0);
-    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
-    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-    const bool         broadcast_bias          = gemm_info.broadcast_bias();
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m                       = m;
-    kernel_info.n                       = n;
-    kernel_info.k                       = k;
-    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    kernel_info.reinterpret_input_as_3d = false;
-    kernel_info.broadcast_bias          = broadcast_bias;
-    kernel_info.activation_info         = gemm_info.activation_info();
-
-    GEMMLHSMatrixInfo lhs_info;
-    GEMMRHSMatrixInfo rhs_info;
-
-    // Pick up the GEMM configuration
-    // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
-    const auto gemm_config = select_default_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
-    lhs_info               = gemm_config.lhs_info;
-    rhs_info               = gemm_config.rhs_info;
-
-    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
-
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
-
-    // Validate matrix multiply
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
-
-    return Status{};
-}
-
-Status CLGEMM::validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(output);
-
-    TensorInfo tmp_b_info{};
-
-    // Get the GPU target
-    const GPUTarget    gpu_target              = CLScheduler::get().target();
-    const DataType     data_type               = a->data_type();
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
-    const unsigned int n                       = b->dimension(0);
-    const unsigned int k                       = a->dimension(0);
-    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
-    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-    const bool         broadcast_bias          = gemm_info.broadcast_bias();
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m                       = m;
-    kernel_info.n                       = n;
-    kernel_info.k                       = k;
-    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
-    kernel_info.broadcast_bias          = broadcast_bias;
-    kernel_info.activation_info         = gemm_info.activation_info();
-
-    GEMMLHSMatrixInfo lhs_info;
-    GEMMRHSMatrixInfo rhs_info;
-
-    // Pick up the GEMM configuration
-    // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
-    const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
-    lhs_info               = gemm_config.lhs_info;
-    rhs_info               = gemm_config.rhs_info;
-
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
-
-    // Validate matrix multiply
-    kernel_info.has_pad_y = false;
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
-
-    kernel_info.has_pad_y = true;
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
-
-    return Status{};
-}
-
 void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     configure(CLKernelLibrary::get().get_compile_context(), a, b, c, output, alpha, beta, gemm_info);
@@ -672,221 +74,56 @@ void CLGEMM::configure(const CLCompileContext &compile_context, const ICLTensor
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
 
-    // Perform validation step
-    ARM_COMPUTE_ERROR_THROW_ON(validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), alpha, beta, gemm_info));
-
-    // Check if we need to reshape the matrix B only on the first run
-    _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
-    _is_prepared                 = gemm_info.retain_internal_weights();
-    _original_b                  = b;
-    _lhs                         = a;
-    _dst                         = output;
-
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
-    const unsigned int n                       = b->info()->dimension(0);
-    const unsigned int k                       = a->info()->dimension(0);
-    const unsigned int batch_size              = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2);
-
-    // Select GEMMType
-    _gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery{ CLScheduler::get().target(), a->info()->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run);
+    _impl->a   = a;
+    _impl->b   = b;
+    _impl->c   = c;
+    _impl->dst = output;
+    _impl->op  = std::make_unique<OperatorType>();
 
-    const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
+    _impl->op->configure(compile_context, a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), alpha, beta, gemm_info);
+    _impl->aux_mem_req = _impl->op->workspace();
 
-    const ICLTensor *c_to_use = fuse_add_c ? c : nullptr;
-
-    switch(_gemm_kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE_V1:
-        {
-            configure_native_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_V1:
-        {
-            configure_reshaped_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED:
-        {
-            configure_reshaped_v2(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        {
-            configure_reshaped_only_rhs(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_ERROR("GEMMType not supported");
-        }
-    }
+    // Manage/allocate auxilairy tensors
+    _impl->run_pack          = { { ACL_SRC_0, _impl->a }, { ACL_SRC_2, _impl->c }, { ACL_DST, _impl->dst } };
+    _impl->prep_pack         = { { ACL_SRC_1, _impl->b } };
+    _impl->workspace_tensors = manage_workspace<CLTensor>(_impl->op->workspace(), _impl->memory_group, _impl->run_pack, _impl->prep_pack);
 }
 
 Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
-    // Get the GPU target
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
-    const unsigned int n                       = b->dimension(0);
-    const unsigned int k                       = a->dimension(0);
-    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
-
-    // Select GEMMType
-    CLGEMMKernelType gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery
-    {
-        CLScheduler::get().target(), a->data_type(), m, n, k, batch_size,
-    },
-    gemm_info.reshape_b_only_on_first_run());
-
-    const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
-
-    const ITensorInfo *c_to_use = fuse_add_c ? c : nullptr;
-
-    switch(gemm_kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE_V1:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_native_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_V1:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped(a, b, c_to_use, output, alpha, beta, gemm_info));
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs(a, b, c_to_use, output, alpha, beta, gemm_info));
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_RETURN_ERROR_MSG("GEMMType not supported");
-        }
-    }
-
-    return Status{};
+    return OperatorType::validate(a, b, c, output, alpha, beta, gemm_info);
 }
 
 void CLGEMM::run()
 {
     prepare();
-    MemoryGroupResourceScope scope_mg(_memory_group);
-
-    // Run matrix multiply kernel
-    switch(_gemm_kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE_V1:
-        {
-            CLScheduler::get().enqueue(*_mm_kernel, true);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_V1:
-        {
-            // Run interleave kernel
-            CLScheduler::get().enqueue(*_reshape_lhs_kernel, false);
 
-            if(!_reshape_b_only_on_first_run)
-            {
-                // Run transpose kernel
-                if(_weights_manager && _weights_manager->are_weights_managed(_original_b))
-                {
-                    _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get());
-                }
-                else
-                {
-                    CLScheduler::get().enqueue(*_reshape_rhs_kernel, false);
-                }
-            }
+    MemoryGroupResourceScope scope_mg(_impl->memory_group);
 
-            CLScheduler::get().enqueue(*_mm_kernel, true);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED:
-        {
-            // Run interleave kernel
-            CLScheduler::get().enqueue(*_reshape_lhs_kernel, false);
-
-            if(!_reshape_b_only_on_first_run)
-            {
-                // Run transpose kernel
-                if(_weights_manager && _weights_manager->are_weights_managed(_original_b))
-                {
-                    _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get());
-                }
-                else
-                {
-                    CLScheduler::get().enqueue(*_reshape_rhs_kernel, false);
-                }
-            }
-
-            CLScheduler::get().enqueue(*_mm_reshaped_kernel, true);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        {
-            if(!_reshape_b_only_on_first_run)
-            {
-                // Run transpose kernel
-                if(_weights_manager && _weights_manager->are_weights_managed(_original_b))
-                {
-                    _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get());
-                }
-                else
-                {
-                    CLScheduler::get().enqueue(*_reshape_rhs_kernel, false);
-                }
-            }
-            // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement
-            // Check if the lhs or dst tensors have padding
-            const unsigned int cross_plane_pad_lhs = _lhs->info()->padding().top + _lhs->info()->padding().bottom;
-            const unsigned int cross_plane_pad_dst = _dst->info()->padding().top + _dst->info()->padding().bottom;
-
-            bool has_pad_y = (cross_plane_pad_lhs != 0) || (cross_plane_pad_dst != 0);
-            if(has_pad_y)
-            {
-                CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_fallback_kernel, true);
-            }
-            else
-            {
-                CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_kernel, true);
-            }
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_ERROR("GEMMType not supported");
-        }
-    }
+    ARM_COMPUTE_ERROR_ON_NULLPTR(_impl->a, _impl->b, _impl->dst);
+    _impl->op->run(_impl->run_pack);
 }
 
 void CLGEMM::prepare()
 {
-    if(!_is_prepared)
+    if(!_impl->_is_prepared)
     {
-        if(_gemm_kernel_type != CLGEMMKernelType::NATIVE_V1 && _reshape_b_only_on_first_run)
+        _impl->op->prepare(_impl->prep_pack);
+
+        auto has_reshape = std::find_if(_impl->aux_mem_req.begin(),
+                                        _impl->aux_mem_req.end(),
+                                        [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
+
+        if(has_reshape != std::end(_impl->aux_mem_req))
+        {
+            _impl->b->mark_as_unused();
+        }
+        else
         {
-            if(_weights_manager && _weights_manager->are_weights_managed(_original_b))
-            {
-                _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get());
-            }
-            else
-            {
-                // Run transpose kernel and mark original weights tensor as unused
-                _tmp_b.allocator()->allocate();
-                CLScheduler::get().enqueue(*_reshape_rhs_kernel, false);
-                _original_b->mark_as_unused();
-            }
+            // Pack the B matrix to be used as the underlying GEMM performs no reshapes
+            _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->b);
         }
-        CLScheduler::get().queue().finish();
-        _is_prepared = true;
+        _impl->_is_prepared = true;
     }
 }
 } // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index f37f06b0ff..5dc7556b2f 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -37,11 +37,6 @@
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/CL/kernels/CLIm2ColKernel.h"
 #include "src/core/CL/kernels/CLWeightsReshapeKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"
diff --git a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
index a040e9d38e..7a01018f59 100644
--- a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -36,11 +36,6 @@
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/CL/kernels/CLIm2ColKernel.h"
 #include "src/core/CL/kernels/CLWeightsReshapeKernel.h"
 
diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
index 5a9ff7990f..099a2c980f 100644
--- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
@@ -40,7 +40,7 @@
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h"
 #include "utils/TypePrinter.h"
@@ -127,7 +127,7 @@ inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs
     TensorInfo tmp_b_info{};
     // Validate reshape RHS kernel
     auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    if(!bool(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info)))
+    if(!bool(opencl::kernels::ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)))
     {
         return false;
     }
@@ -192,7 +192,7 @@ CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemo
       _weights_to_qasymm8(std::make_unique<CLDepthConvertLayerKernel>()),
       _mm_native_kernel(std::make_unique<CLGEMMLowpMatrixMultiplyNativeKernel>()),
       _mm_reshaped_only_rhs_kernel(std::make_unique<CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel>()),
-      _mtx_b_reshape_kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>()),
+      _mtx_b_reshape_kernel(std::make_unique<opencl::kernels::ClGemmReshapeRhsMatrixKernel>()),
       _mtx_a_reduction_kernel(std::make_unique<CLGEMMLowpMatrixAReductionKernel>()),
       _mtx_b_reduction_kernel(std::make_unique<CLGEMMLowpMatrixBReductionKernel>()),
       _offset_contribution_kernel(std::make_unique<CLGEMMLowpOffsetContributionKernel>()),
@@ -292,7 +292,7 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const CLCompileContext &compile_con
                                                                                  a->info(), _convert_to_qasymm8 ? _qasymm8_weights.info() : b->info(), output->info());
 
         // Configure reshape RHS kernel
-        _mtx_b_reshape_kernel->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : b, &_tmp_b, rhs_info);
+        _mtx_b_reshape_kernel->configure(compile_context, _convert_to_qasymm8 ? _qasymm8_weights.info() : b->info(), _tmp_b.info(), rhs_info);
     }
 
     // Using default reduction info
@@ -496,7 +496,7 @@ Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso
 
         // Validate reshape RHS kernel
         auto_init_if_empty(tmp_b_info, weights_info.clone()->set_tensor_shape(compute_rhs_reshaped_shape(weights_info, rhs_info)));
-        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(&weights_info, &tmp_b_info, rhs_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(opencl::kernels::ClGemmReshapeRhsMatrixKernel::validate(&weights_info, &tmp_b_info, rhs_info));
     }
 
     TensorInfo info_vector_sum_col{};
@@ -634,6 +634,9 @@ void CLGEMMLowpMatrixMultiplyCore::run()
         if(!_reshape_b_only_on_first_run)
         {
             // Run reshape matrix B
+            ITensorPack mtx_b_pack;
+            mtx_b_pack.add_const_tensor(TensorType::ACL_SRC, _convert_to_qasymm8 ? &_qasymm8_weights : _original_b);
+            mtx_b_pack.add_tensor(TensorType::ACL_DST, &_tmp_b);
             CLScheduler::get().enqueue(*_mtx_b_reshape_kernel, false);
         }
     }
@@ -687,7 +690,10 @@ void CLGEMMLowpMatrixMultiplyCore::prepare()
 
             // Run reshape kernel and mark original weights tensor as unused
             _tmp_b.allocator()->allocate();
-            CLScheduler::get().enqueue(*_mtx_b_reshape_kernel, false);
+            ITensorPack mtx_b_pack;
+            mtx_b_pack.add_const_tensor(TensorType::ACL_SRC, _convert_to_qasymm8 ? &_qasymm8_weights : _original_b);
+            mtx_b_pack.add_tensor(TensorType::ACL_DST, &_tmp_b);
+            CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_pack, false);
             _original_b->mark_as_unused();
         }
 
diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp
index 05d459c899..146ac8f619 100644
--- a/src/runtime/CL/functions/CLLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLLSTMLayer.cpp
@@ -36,11 +36,6 @@
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/gpu/cl/kernels/ClTransposeKernel.h"
 
 namespace arm_compute
diff --git a/src/runtime/CL/functions/CLLSTMLayerQuantized.cpp b/src/runtime/CL/functions/CLLSTMLayerQuantized.cpp
index 46062387e7..69974424c9 100644
--- a/src/runtime/CL/functions/CLLSTMLayerQuantized.cpp
+++ b/src/runtime/CL/functions/CLLSTMLayerQuantized.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -34,7 +34,6 @@
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"
 
 #include <memory>
diff --git a/src/runtime/CL/functions/CLQLSTMLayer.cpp b/src/runtime/CL/functions/CLQLSTMLayer.cpp
index e7a0e5765e..7b6ec8f5c8 100644
--- a/src/runtime/CL/functions/CLQLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLQLSTMLayer.cpp
@@ -37,7 +37,6 @@
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.h"
 #include "src/core/helpers/WindowHelpers.h"
 
diff --git a/src/runtime/CL/functions/CLRNNLayer.cpp b/src/runtime/CL/functions/CLRNNLayer.cpp
index 967f4aa41b..45ced35782 100644
--- a/src/runtime/CL/functions/CLRNNLayer.cpp
+++ b/src/runtime/CL/functions/CLRNNLayer.cpp
@@ -35,11 +35,6 @@
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 
 namespace arm_compute
 {
diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
index e47537bd31..fe45f65beb 100644
--- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp
+++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
@@ -43,7 +43,7 @@ struct CLSoftmaxLayerGeneric<IS_LOG>::Impl
     ICLTensor                    *dst{ nullptr };
     std::unique_ptr<OperatorType> op{ nullptr };
     MemoryGroup                   memory_group{};
-    std::vector<std::pair<TensorType, std::unique_ptr<CLTensor>>> workspace_tensors{};
+    std::vector<std::pair<int, std::unique_ptr<CLTensor>>> workspace_tensors{};
 };
 
 template <bool IS_LOG>
@@ -88,14 +88,14 @@ void           CLSoftmaxLayerGeneric<IS_LOG>::allocate_workspace()
     std::for_each(memory_requirements.begin(), memory_requirements.end(), [this](const experimental::MemoryInfo & memory_info)
     {
         auto tensor_info = TensorInfo{ TensorShape(memory_info.size), 1, DataType::U8 };
-        _impl->workspace_tensors.emplace_back(memory_info.type, std::make_unique<CLTensor>());
+        _impl->workspace_tensors.emplace_back(memory_info.slot, std::make_unique<CLTensor>());
         auto tensor = _impl->workspace_tensors.back().second.get();
         ARM_COMPUTE_ERROR_ON_NULLPTR(tensor);
         tensor->allocator()->init(tensor_info);
         _impl->memory_group.manage(tensor);
     });
 
-    std::for_each(_impl->workspace_tensors.begin(), _impl->workspace_tensors.end(), [](std::pair<TensorType, std::unique_ptr<CLTensor>> &wt)
+    std::for_each(_impl->workspace_tensors.begin(), _impl->workspace_tensors.end(), [](std::pair<int, std::unique_ptr<CLTensor>> &wt)
     {
         auto tensor = wt.second.get();
         tensor->allocator()->allocate();
@@ -114,7 +114,7 @@ void           CLSoftmaxLayerGeneric<IS_LOG>::run()
     pack.add_tensor(TensorType::ACL_SRC, _impl->src);
     pack.add_tensor(TensorType::ACL_DST, _impl->dst);
 
-    std::for_each(_impl->workspace_tensors.begin(), _impl->workspace_tensors.end(), [&pack](std::pair<TensorType, std::unique_ptr<CLTensor>> &wt)
+    std::for_each(_impl->workspace_tensors.begin(), _impl->workspace_tensors.end(), [&pack](std::pair<int, std::unique_ptr<CLTensor>> &wt)
     {
         auto tensor = wt.second.get();
         ARM_COMPUTE_ERROR_ON_NULLPTR(tensor);
diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
index 321466f05f..6b8b00414a 100644
--- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2020 Arm Limited.
+ * Copyright (c) 2018-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -29,11 +29,6 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/CL/kernels/CLWinogradFilterTransformKernel.h"
 #include "src/core/CL/kernels/CLWinogradOutputTransformKernel.h"
 
diff --git a/src/runtime/CL/gemm/CLGEMMDefaultTypeBifrost.cpp b/src/runtime/CL/gemm/CLGEMMDefaultTypeBifrost.cpp
index 5ac25a9a20..390bb97665 100644
--- a/src/runtime/CL/gemm/CLGEMMDefaultTypeBifrost.cpp
+++ b/src/runtime/CL/gemm/CLGEMMDefaultTypeBifrost.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020 Arm Limited.
+ * Copyright (c) 2020-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -25,7 +25,7 @@
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <map>
 #include <utility>
diff --git a/src/runtime/CL/gemm/CLGEMMDefaultTypeMidgard.cpp b/src/runtime/CL/gemm/CLGEMMDefaultTypeMidgard.cpp
index 88b6060e12..b799de6967 100644
--- a/src/runtime/CL/gemm/CLGEMMDefaultTypeMidgard.cpp
+++ b/src/runtime/CL/gemm/CLGEMMDefaultTypeMidgard.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020 Arm Limited.
+ * Copyright (c) 2020-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -26,7 +26,7 @@
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/GPUTarget.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <map>
 #include <utility>
diff --git a/src/runtime/CL/gemm/CLGEMMDefaultTypeValhall.cpp b/src/runtime/CL/gemm/CLGEMMDefaultTypeValhall.cpp
index 0f754276c7..982748810d 100644
--- a/src/runtime/CL/gemm/CLGEMMDefaultTypeValhall.cpp
+++ b/src/runtime/CL/gemm/CLGEMMDefaultTypeValhall.cpp
@@ -25,7 +25,7 @@
 
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
 
 #include <map>
 #include <utility>
diff --git a/src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.cpp b/src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.cpp
index 489be356d9..b8437487f8 100644
--- a/src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.cpp
+++ b/src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.cpp
@@ -27,11 +27,11 @@
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "arm_compute/runtime/CL/ICLGEMMKernelSelection.h"
-#include "src/core/CL/ICLGEMMKernelConfiguration.h"
-#include "src/core/CL/gemm/CLGEMMHelpers.h"
-#include "src/core/CL/gemm/native/CLGEMMNativeKernelConfiguration.h"
-#include "src/core/CL/gemm/reshaped/CLGEMMReshapedKernelConfiguration.h"
-#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfiguration.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
+#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h"
+#include "src/core/gpu/cl/kernels/gemm/native/ClGemmNativeKernelConfig.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmReshapedKernelConfig.h"
+#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmReshapedOnlyRhsKernelConfig.h"
 #include "src/runtime/CL/gemm/CLGEMMKernelSelection.h"
 #include "src/runtime/CL/mlgo/MLGOHeuristics.h"
 #include "src/runtime/CL/mlgo/Utils.h"
@@ -43,6 +43,8 @@ namespace cl_gemm
 {
 namespace auto_heuristics
 {
+using namespace arm_compute::opencl::kernels::gemm;
+
 GEMMTypeResult select_mlgo_gemm_kernel(const CommonQuery &query, bool reshape_b_only_on_first_run)
 {
     ARM_COMPUTE_UNUSED(reshape_b_only_on_first_run);
@@ -83,9 +85,9 @@ GEMMTypeResult select_default_gemm_kernel(const CommonQuery &query, bool reshape
 
 GEMMConfigResult select_default_gemm_config_reshaped_only_rhs(const CommonQuery &query)
 {
-    GEMMLHSMatrixInfo                           lhs_info;
-    GEMMRHSMatrixInfo                           rhs_info;
-    std::unique_ptr<ICLGEMMKernelConfiguration> gemm_config = CLGEMMReshapedOnlyRHSKernelConfigurationFactory::create(query.gpu_target);
+    GEMMLHSMatrixInfo                    lhs_info;
+    GEMMRHSMatrixInfo                    rhs_info;
+    std::unique_ptr<IClGemmKernelConfig> gemm_config = ClGemmReshapedOnlyRhsKernelConfigurationFactory::create(query.gpu_target);
     ARM_COMPUTE_ERROR_ON_NULLPTR(gemm_config.get());
     std::tie(lhs_info, rhs_info) = gemm_config->configure(query.m, query.n, query.k, query.b, query.data_type);
     return GEMMConfigResult{ true, lhs_info, rhs_info };
@@ -118,9 +120,9 @@ GEMMConfigResult select_mlgo_gemm_config_reshaped_only_rhs(const CommonQuery &qu
 
 GEMMConfigResult select_default_gemm_config_reshaped(const CommonQuery &query)
 {
-    GEMMLHSMatrixInfo                           lhs_info;
-    GEMMRHSMatrixInfo                           rhs_info;
-    std::unique_ptr<ICLGEMMKernelConfiguration> gemm_config = CLGEMMReshapedKernelConfigurationFactory::create(query.gpu_target);
+    GEMMLHSMatrixInfo                    lhs_info;
+    GEMMRHSMatrixInfo                    rhs_info;
+    std::unique_ptr<IClGemmKernelConfig> gemm_config = ClGemmReshapedKernelConfigurationFactory::create(query.gpu_target);
     ARM_COMPUTE_ERROR_ON_NULLPTR(gemm_config.get());
     std::tie(lhs_info, rhs_info) = gemm_config->configure(query.m, query.n, query.k, query.b, query.data_type);
     return GEMMConfigResult{ true, lhs_info, rhs_info };
@@ -152,9 +154,9 @@ GEMMConfigResult select_mlgo_gemm_config_reshaped(const CommonQuery &query)
 
 GEMMConfigResult select_default_gemm_config_native(const CommonQuery &query)
 {
-    GEMMLHSMatrixInfo                           lhs_info;
-    GEMMRHSMatrixInfo                           rhs_info;
-    std::unique_ptr<ICLGEMMKernelConfiguration> gemm_config = CLGEMMNativeKernelConfigurationFactory::create(query.gpu_target);
+    GEMMLHSMatrixInfo                    lhs_info;
+    GEMMRHSMatrixInfo                    rhs_info;
+    std::unique_ptr<IClGemmKernelConfig> gemm_config = ClGemmNativeKernelConfigurationFactory::create(query.gpu_target);
     ARM_COMPUTE_ERROR_ON_NULLPTR(gemm_config.get());
     std::tie(lhs_info, rhs_info) = gemm_config->configure(query.m, query.n, query.k, query.b, query.data_type);
     return GEMMConfigResult{ true, lhs_info, rhs_info };
@@ -175,7 +177,7 @@ GEMMConfigResult select_mlgo_gemm_config_native(const CommonQuery &query)
     {
         ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("MLGOHeuristics query returns gemm config: %s.", to_string(config).c_str());
         // Setting irrelevant unsigned int parameters to 1 and bool parameters to false as they do no matter
-        std::tie(lhs_info, rhs_info) = configure_lhs_rhs_info(query.m, query.n, config.m0, config.n0, config.k0, 1, 1, false, false, false, false, false);
+        std::tie(lhs_info, rhs_info) = opencl::kernels::gemm::configure_lhs_rhs_info(query.m, query.n, config.m0, config.n0, config.k0, 1, 1, false, false, false, false, false);
     }
     else
     {
diff --git a/src/runtime/ITensorAllocator.cpp b/src/runtime/ITensorAllocator.cpp
index ae648d4dd2..fe3d2804cb 100644
--- a/src/runtime/ITensorAllocator.cpp
+++ b/src/runtime/ITensorAllocator.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016-2018 Arm Limited.
+ * Copyright (c) 2016-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -30,25 +30,27 @@
 
 using namespace arm_compute;
 
-ITensorAllocator::ITensorAllocator()
-    : _info(), _alignment(0)
+void ITensorAllocator::init(const TensorInfo &input, size_t alignment)
 {
+    _info_owned    = input;
+    _info_external = nullptr;
+    _alignment     = alignment;
 }
 
-void ITensorAllocator::init(const TensorInfo &input, size_t alignment)
+void ITensorAllocator::soft_init(TensorInfo &input, size_t alignment)
 {
-    _info      = input;
-    _alignment = alignment;
+    _info_external = &input;
+    _alignment     = alignment;
 }
 
 TensorInfo &ITensorAllocator::info()
 {
-    return _info;
+    return (_info_external != nullptr) ? *_info_external : _info_owned;
 }
 
 const TensorInfo &ITensorAllocator::info() const
 {
-    return _info;
+    return (_info_external != nullptr) ? *_info_external : _info_owned;
 }
 
 size_t ITensorAllocator::alignment() const
diff --git a/src/runtime/gpu/cl/operators/ClGemm.cpp b/src/runtime/gpu/cl/operators/ClGemm.cpp
new file mode 100644
index 0000000000..fcbc6d5fba
--- /dev/null
+++ b/src/runtime/gpu/cl/operators/ClGemm.cpp
@@ -0,0 +1,754 @@
+/*
+ * Copyright (c) 2017-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/runtime/gpu/cl/operators/ClGemm.h"
+
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/GPUTarget.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/KernelDescriptors.h"
+#include "arm_compute/core/Log.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/ITensorAllocator.h"
+#include "src/core/gpu/cl/IClKernel.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/core/utils/helpers/float_ops.h"
+#include "src/runtime/CL/gemm/CLGEMMKernelSelection.h"
+#include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h"
+#include "src/runtime/gpu/cl/utils/ClAuxTensorHandler.h"
+
+#include "support/Cast.h"
+#include "utils/TypePrinter.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::cl_gemm;
+using namespace arm_compute::experimental;
+using namespace arm_compute::utils::cast;
+using namespace arm_compute::opencl::kernels;
+
+namespace
+{
+inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type)
+{
+    switch(kernel_type)
+    {
+        case CLGEMMKernelType::NATIVE_V1:
+        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
+        case CLGEMMKernelType::RESHAPED_V1:
+        case CLGEMMKernelType::RESHAPED:
+        {
+            return true;
+        }
+        default:
+        {
+            return false;
+        }
+    }
+}
+//Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type
+inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run)
+{
+    auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run);
+    if(bool(gemm_kernel))
+    {
+        if(validate_gemm_kernel(gemm_kernel.gemm_type))
+        {
+            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str());
+            return gemm_kernel.gemm_type;
+        }
+    }
+    gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run);
+    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str());
+    return gemm_kernel.gemm_type;
+}
+// Validate lhs_info and rhs_info for reshaped only rhs kernel
+inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c,
+                                                    const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info)
+{
+    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel
+    TensorInfo tmp_b_info{};
+    // Validate reshape RHS kernel
+    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
+    if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)))
+    {
+        return false;
+    }
+    // Validate mm kernel
+    gemm_kernel_info.lhs_info  = lhs_info;
+    gemm_kernel_info.rhs_info  = rhs_info;
+    gemm_kernel_info.has_pad_y = false;
+    if(!bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
+    {
+        return false;
+    }
+    gemm_kernel_info.has_pad_y = true;
+    if(!bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
+    {
+        return false;
+    }
+    return true;
+}
+
+//Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs
+inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a,
+                                                                                                 const ITensorInfo *b,
+                                                                                                 const ITensorInfo *c, const ITensorInfo *output)
+{
+    auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query);
+    if(config)
+    {
+        if(validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info))
+        {
+            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
+            return { config.lhs_info, config.rhs_info };
+        }
+    }
+    config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query);
+    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
+    return { config.lhs_info, config.rhs_info };
+}
+
+// Validate lhs_info and rhs_info for reshaped kernel
+inline bool validate_lhs_rhs_info_reshaped(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c,
+                                           const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info, bool reinterpret_input_as_3d)
+{
+    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped kernel
+    TensorInfo tmp_a_info{};
+    TensorInfo tmp_b_info{};
+
+    // Validate reshape LHS kernel
+    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, reinterpret_input_as_3d)));
+    if(!bool(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, reinterpret_input_as_3d)))
+    {
+        return false;
+    }
+
+    // Validate reshape RHS kernel
+    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
+    if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)))
+    {
+        return false;
+    }
+    // Validate mm kernel
+    gemm_kernel_info.lhs_info = lhs_info;
+    gemm_kernel_info.rhs_info = rhs_info;
+    if(!bool(ClGemmMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
+    {
+        return false;
+    }
+    return true;
+}
+
+//Automatically select between mlgo (prioritized) and default heuristics for reshaped kernel configs
+inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, const ITensorInfo *b,
+                                                                                        const ITensorInfo *c, const ITensorInfo *output, bool reinterpret_input_as_3d)
+{
+    auto config = auto_heuristics::select_mlgo_gemm_config_reshaped(query);
+    if(config)
+    {
+        if(validate_lhs_rhs_info_reshaped(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info, reinterpret_input_as_3d))
+        {
+            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
+            return { config.lhs_info, config.rhs_info };
+        }
+    }
+    config = auto_heuristics::select_default_gemm_config_reshaped(query);
+    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
+    return { config.lhs_info, config.rhs_info };
+}
+} // namespace
+
+ClGemm::ClGemm()
+    : _mm_kernel(std::make_unique<ClGemmMatrixMultiplyKernel>()),
+      _reshape_lhs_kernel(std::make_unique<ClGemmReshapeLhsMatrixKernel>()),
+      _reshape_rhs_kernel(std::make_unique<ClGemmReshapeRhsMatrixKernel>()),
+      _mm_reshaped_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedKernel>()),
+      _mm_reshaped_only_rhs_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedOnlyRhsKernel>()),
+      _mm_reshaped_only_rhs_fallback_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedOnlyRhsKernel>()),
+      _tmp_a(),
+      _tmp_b(),
+      _reshape_b_only_on_first_run(false),
+      _gemm_kernel_type(CLGEMMKernelType::NATIVE_V1),
+      _aux_mem(AuxTensorIdx::Count)
+{
+}
+
+void ClGemm::configure_native_v1(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
+                                 const GEMMInfo &gemm_info)
+{
+    const unsigned int m          = gemm_info.reinterpret_input_as_3d() ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n          = b->dimension(0);
+    const unsigned int k          = a->dimension(0);
+    const GPUTarget    gpu_target = CLScheduler::get().target();
+
+    // Set the target for the kernels
+    _mm_kernel->set_target(gpu_target);
+
+    GEMMReshapeInfo reshape_info(m, n, k, 1, 1, gemm_info.depth_output_gemm3d(), gemm_info.reinterpret_input_as_3d(), gemm_info.broadcast_bias());
+
+    // Configure and tune matrix multiply kernel
+    _mm_kernel->configure(compile_context, a, b, c, output, alpha, beta, false, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info());
+
+    // Tune kernel statically
+    CLScheduler::get().tune_kernel_static(*_mm_kernel);
+}
+
+void ClGemm::configure_reshaped_v1(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
+                                   const GEMMInfo &gemm_info)
+{
+    bool               reinterpret_input_as_3d   = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                         = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                         = b->dimension(0);
+    const unsigned int k                         = a->dimension(0);
+    const int          depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
+    const GPUTarget    gpu_target                = CLScheduler::get().target();
+    int                mult_transpose1xW_width   = 1;
+    int                mult_interleave4x4_height = 1;
+
+    // Set the target for the kernels
+    _reshape_lhs_kernel->set_target(gpu_target);
+    _mm_kernel->set_target(gpu_target);
+
+    if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
+    {
+        mult_transpose1xW_width   = 4;
+        mult_interleave4x4_height = 2;
+    }
+
+    GEMMRHSMatrixInfo rhs_info;
+    rhs_info.n0         = 16 / b->element_size();
+    rhs_info.k0         = 1;
+    rhs_info.h0         = mult_transpose1xW_width;
+    rhs_info.interleave = false;
+    rhs_info.transpose  = false;
+
+    GEMMLHSMatrixInfo lhs_info;
+    lhs_info.m0         = 4;
+    lhs_info.k0         = 4;
+    lhs_info.v0         = mult_interleave4x4_height;
+    lhs_info.interleave = true;
+    lhs_info.transpose  = true;
+
+    GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias());
+
+    // Configure interleave kernel
+    _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, reinterpret_input_as_3d);
+
+    // Configure transpose kernel
+    _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
+
+    // Configure and tune matrix multiply kernel
+    _mm_kernel->configure(compile_context, &_tmp_a, &_tmp_b, c, output, alpha, beta, true, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info());
+
+    CLScheduler::get().tune_kernel_static(*_mm_kernel);
+
+    // Request memory for LHS and RHS reshape matrix
+    _aux_mem[LhsReshape] = MemoryInfo(offset_int_vec(LhsReshape), MemoryLifetime::Temporary, _tmp_a.total_size());
+    _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size());
+}
+
+void ClGemm::configure_reshaped_v2(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
+                                   const GEMMInfo &gemm_info)
+{
+    DataType           data_type               = a->data_type();
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
+    const GPUTarget    gpu_target              = CLScheduler::get().target();
+    bool               broadcast_bias          = gemm_info.broadcast_bias();
+
+    GEMMKernelInfo kernel_info;
+    kernel_info.m                       = m;
+    kernel_info.n                       = n;
+    kernel_info.k                       = k;
+    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
+    kernel_info.reinterpret_input_as_3d = false;
+    kernel_info.broadcast_bias          = broadcast_bias;
+    kernel_info.activation_info         = gemm_info.activation_info();
+
+    // Set the target for the kernels
+    _reshape_lhs_kernel->set_target(gpu_target);
+    _mm_kernel->set_target(gpu_target);
+
+    GEMMLHSMatrixInfo lhs_info{};
+    GEMMRHSMatrixInfo rhs_info{};
+
+    // Pick up the GEMM configuration
+    std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a, b,
+                                                                    c, output, gemm_info.reinterpret_input_as_3d());
+
+    _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d());
+    _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
+
+    // Configure and tune matrix multiply kernel
+    _mm_reshaped_kernel->configure(compile_context, &_tmp_a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
+
+    // Request memory for LHS and RHS reshape matrix
+    _aux_mem[LhsReshape] = MemoryInfo(offset_int_vec(LhsReshape), MemoryLifetime::Temporary, _tmp_a.total_size());
+    _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size());
+}
+
+void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
+                                         const GEMMInfo &gemm_info)
+{
+    DataType           data_type               = a->data_type();
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
+    const GPUTarget    gpu_target              = CLScheduler::get().target();
+    bool               broadcast_bias          = gemm_info.broadcast_bias();
+
+    GEMMKernelInfo kernel_info;
+    kernel_info.m                       = m;
+    kernel_info.n                       = n;
+    kernel_info.k                       = k;
+    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
+    kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
+    kernel_info.broadcast_bias          = broadcast_bias;
+    kernel_info.activation_info         = gemm_info.activation_info();
+
+    // Set the target for the kernels
+    _mm_kernel->set_target(gpu_target);
+
+    GEMMLHSMatrixInfo lhs_info{};
+    GEMMRHSMatrixInfo rhs_info{};
+
+    // Pick up the GEMM configuration
+    std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a, b, c, output);
+
+    // Transpose matrix
+    _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
+
+    // Configure two variants of CLGEMMMatrixMultiplyReshapedOnlyRHSKernel (has_pad_y = false/true)
+    // During the prepare stage we check the padding requirement for the lhs and dst tensors. If they do not have
+    // pad y, we dispatch CLGEMMMatrixMultiplyReshapedOnlyRHSKernel with has_pad_y = false
+
+    // Configure matrix multiply kernel with no y padding support
+    kernel_info.has_pad_y = false;
+    _mm_reshaped_only_rhs_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
+
+    // Configure matrix multiply kernel with y padding support
+    kernel_info.has_pad_y = true;
+    _mm_reshaped_only_rhs_fallback_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
+
+    // Request memory for RHS reshape matrix
+    _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size());
+}
+
+Status ClGemm::validate_native_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_UNUSED(alpha);
+    ARM_COMPUTE_UNUSED(output);
+
+    // Get the GPU target
+    const GPUTarget    gpu_target              = CLScheduler::get().target();
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
+
+    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d, gemm_info.broadcast_bias());
+
+    // Validate matrix multiply
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyKernel::validate(a, b, c, output, alpha, beta,
+                                                                     false, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info()));
+
+    return Status{};
+}
+
+Status ClGemm::validate_reshaped_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_UNUSED(alpha);
+    ARM_COMPUTE_UNUSED(output);
+
+    TensorInfo tmp_a_info{};
+    TensorInfo tmp_b_info{};
+
+    // Get the GPU target
+    const GPUTarget    gpu_target                = CLScheduler::get().target();
+    const unsigned int m                         = gemm_info.reinterpret_input_as_3d() ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                         = b->dimension(0);
+    const unsigned int k                         = a->dimension(0);
+    int                mult_transpose1xW_width   = 1;
+    int                mult_interleave4x4_height = 1;
+    const int          depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
+
+    if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
+    {
+        mult_transpose1xW_width   = 4;
+        mult_interleave4x4_height = 2;
+    }
+
+    GEMMRHSMatrixInfo rhs_info;
+    rhs_info.n0         = 16 / b->element_size();
+    rhs_info.k0         = 1;
+    rhs_info.h0         = mult_transpose1xW_width;
+    rhs_info.interleave = false;
+    rhs_info.transpose  = false;
+
+    GEMMLHSMatrixInfo lhs_info;
+    lhs_info.m0         = 4;
+    lhs_info.k0         = 4;
+    lhs_info.v0         = mult_interleave4x4_height;
+    lhs_info.interleave = true;
+    lhs_info.transpose  = true;
+
+    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias());
+
+    // Validate interleave kernel
+    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
+
+    // Validate transpose kernel
+    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info));
+
+    // Validate matrix multiply
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta,
+                                                                     true, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info()));
+
+    return Status{};
+}
+
+Status ClGemm::validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_UNUSED(alpha);
+    ARM_COMPUTE_UNUSED(output);
+
+    TensorInfo tmp_a_info{};
+    TensorInfo tmp_b_info{};
+
+    // Get the GPU target
+    const GPUTarget    gpu_target              = CLScheduler::get().target();
+    DataType           data_type               = a->data_type();
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
+    const bool         broadcast_bias          = gemm_info.broadcast_bias();
+
+    GEMMKernelInfo kernel_info;
+    kernel_info.m                       = m;
+    kernel_info.n                       = n;
+    kernel_info.k                       = k;
+    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
+    kernel_info.reinterpret_input_as_3d = false;
+    kernel_info.broadcast_bias          = broadcast_bias;
+    kernel_info.activation_info         = gemm_info.activation_info();
+
+    GEMMLHSMatrixInfo lhs_info;
+    GEMMRHSMatrixInfo rhs_info;
+
+    // Pick up the GEMM configuration
+    // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
+    const auto gemm_config = select_default_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
+    lhs_info               = gemm_config.lhs_info;
+    rhs_info               = gemm_config.rhs_info;
+
+    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
+
+    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info));
+
+    // Validate matrix multiply
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
+
+    return Status{};
+}
+
+Status ClGemm::validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_UNUSED(alpha);
+    ARM_COMPUTE_UNUSED(output);
+
+    TensorInfo tmp_b_info{};
+
+    // Get the GPU target
+    const GPUTarget    gpu_target              = CLScheduler::get().target();
+    const DataType     data_type               = a->data_type();
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
+    const bool         broadcast_bias          = gemm_info.broadcast_bias();
+
+    GEMMKernelInfo kernel_info;
+    kernel_info.m                       = m;
+    kernel_info.n                       = n;
+    kernel_info.k                       = k;
+    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
+    kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
+    kernel_info.broadcast_bias          = broadcast_bias;
+    kernel_info.activation_info         = gemm_info.activation_info();
+
+    GEMMLHSMatrixInfo lhs_info;
+    GEMMRHSMatrixInfo rhs_info;
+
+    // Pick up the GEMM configuration
+    // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
+    const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
+    lhs_info               = gemm_config.lhs_info;
+    rhs_info               = gemm_config.rhs_info;
+
+    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info));
+
+    // Validate matrix multiply
+    kernel_info.has_pad_y = false;
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
+
+    kernel_info.has_pad_y = true;
+    ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
+
+    return Status{};
+}
+
+void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
+
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(validate(a, b, c, output, alpha, beta, gemm_info));
+
+    // Check if we need to reshape the matrix B only on the first run
+    _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
+
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+
+    // Select GEMMType
+    _gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery{ CLScheduler::get().target(), a->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run);
+
+    const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
+
+    ITensorInfo *c_to_use = fuse_add_c ? c : nullptr;
+
+    switch(_gemm_kernel_type)
+    {
+        case CLGEMMKernelType::NATIVE_V1:
+        {
+            configure_native_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
+            break;
+        }
+        case CLGEMMKernelType::RESHAPED_V1:
+        {
+            configure_reshaped_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
+            break;
+        }
+        case CLGEMMKernelType::RESHAPED:
+        {
+            configure_reshaped_v2(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
+            break;
+        }
+        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
+        {
+            configure_reshaped_only_rhs(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("GEMMType not supported");
+        }
+    }
+}
+
+Status ClGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
+{
+    // Get the GPU target
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+
+    // Select GEMMType
+    CLGEMMKernelType gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery
+    {
+        CLScheduler::get().target(), a->data_type(), m, n, k, batch_size,
+    },
+    gemm_info.reshape_b_only_on_first_run());
+
+    const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
+
+    const ITensorInfo *c_to_use = fuse_add_c ? c : nullptr;
+
+    switch(gemm_kernel_type)
+    {
+        case CLGEMMKernelType::NATIVE_V1:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_native_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
+            break;
+        }
+        case CLGEMMKernelType::RESHAPED_V1:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
+            break;
+        }
+        case CLGEMMKernelType::RESHAPED:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped(a, b, c_to_use, output, alpha, beta, gemm_info));
+            break;
+        }
+        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs(a, b, c_to_use, output, alpha, beta, gemm_info));
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_RETURN_ERROR_MSG("GEMMType not supported");
+        }
+    }
+
+    return Status{};
+}
+
+void ClGemm::run(ITensorPack &tensors)
+{
+    const ITensor *lhs  = tensors.get_const_tensor(ACL_SRC_0);
+    const ITensor *rhs  = tensors.get_const_tensor(ACL_SRC_1);
+    const ITensor *src2 = tensors.get_const_tensor(ACL_SRC_2);
+    ITensor       *dst  = tensors.get_tensor(ACL_DST);
+
+    ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, dst);
+
+    CLAuxTensorHandler lhs_reshaped(offset_int_vec(LhsReshape), _tmp_a, tensors, true);
+    CLAuxTensorHandler rhs_reshaped(offset_int_vec(RhsReshape), _tmp_b, tensors, true);
+
+    // Prepare the consts if needed
+    prepare(tensors);
+
+    // Run matrix multiply kernel
+    switch(_gemm_kernel_type)
+    {
+        case CLGEMMKernelType::NATIVE_V1:
+        {
+            CLScheduler::get().enqueue_op(*_mm_kernel, tensors, true);
+            break;
+        }
+        case CLGEMMKernelType::RESHAPED_V1:
+        case CLGEMMKernelType::RESHAPED:
+        {
+            // Run interleave kernel
+            ITensorPack reshape_lhs_pack{ { ACL_SRC, lhs }, { ACL_DST, lhs_reshaped.get() } };
+            CLScheduler::get().enqueue_op(*_reshape_lhs_kernel, reshape_lhs_pack, false);
+
+            if(!_reshape_b_only_on_first_run)
+            {
+                // Run transpose kernel
+                ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } };
+                CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false);
+            }
+
+            ITensorPack gemm_reshaped_pack{ { ACL_SRC_0, lhs_reshaped.get() }, { ACL_SRC_1, rhs_reshaped.get() }, { ACL_SRC_2, src2 }, { ACL_DST, dst } };
+            if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED)
+            {
+                CLScheduler::get().enqueue_op(*_mm_reshaped_kernel, gemm_reshaped_pack, true);
+            }
+            else
+            {
+                CLScheduler::get().enqueue_op(*_mm_kernel, gemm_reshaped_pack, true);
+            }
+            break;
+        }
+        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
+        {
+            if(!_reshape_b_only_on_first_run)
+            {
+                // Run transpose kernel
+                ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } };
+                CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false);
+            }
+            // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement
+            // Check if the lhs or dst tensors have padding
+            const unsigned int cross_plane_pad_lhs = lhs->info()->padding().top + lhs->info()->padding().bottom;
+            const unsigned int cross_plane_pad_dst = dst->info()->padding().top + dst->info()->padding().bottom;
+            bool               has_pad_y           = (cross_plane_pad_lhs != 0) || (cross_plane_pad_dst != 0);
+
+            ITensorPack gemm_reshaped_onlyrhs_pack{ { ACL_SRC_0, lhs }, { ACL_SRC_1, rhs_reshaped.get() }, { ACL_SRC_2, src2 }, { ACL_DST, dst } };
+            if(has_pad_y)
+            {
+                CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_fallback_kernel, gemm_reshaped_onlyrhs_pack, true);
+            }
+            else
+            {
+                CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_kernel, gemm_reshaped_onlyrhs_pack, true);
+            }
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("GEMMType not supported");
+        }
+    }
+}
+
+void ClGemm::prepare(ITensorPack &constants)
+{
+    const ITensor *src1    = constants.get_const_tensor(ACL_SRC_1);
+    ICLTensor     *rhs_aux = utils::cast::polymorphic_downcast<ICLTensor *>(constants.get_tensor(offset_int_vec(RhsReshape)));
+
+    // If memory for RHS is persistent and src1 is provided re-transform else assume that RHS is transformed
+    if((_aux_mem[AuxTensorIdx::RhsReshape].lifetime == MemoryLifetime::Persistent) && (src1 != nullptr && rhs_aux != nullptr) && rhs_aux)
+    {
+        CLAuxTensorHandler rhs_reshaped(_tmp_b, *rhs_aux);
+        ARM_COMPUTE_ERROR_ON(rhs_reshaped.get()->cl_buffer().get() == nullptr);
+
+        ITensorPack reshape_rhs_pack{ { ACL_SRC, src1 }, { ACL_DST, rhs_reshaped.get() } };
+        CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, true);
+    }
+}
+
+experimental::MemoryRequirements ClGemm::workspace() const
+{
+    return _aux_mem;
+}
+} // namespace opencl
+} // namespace arm_compute
diff --git a/src/runtime/gpu/cl/operators/ClGemm.h b/src/runtime/gpu/cl/operators/ClGemm.h
new file mode 100644
index 0000000000..bd9ca17edf
--- /dev/null
+++ b/src/runtime/gpu/cl/operators/ClGemm.h
@@ -0,0 +1,136 @@
+/*
+ * Copyright (c) 2016-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_GEMM_H
+#define ARM_COMPUTE_CL_GEMM_H
+
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/CLTypes.h"
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/core/gpu/cl/IClKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
+#include "src/runtime/gpu/cl/IClOperator.h"
+
+#include <memory>
+
+namespace arm_compute
+{
+namespace opencl
+{
+/** Basic function to execute GEMM on OpenCL. This function calls the following OpenCL kernels:
+ *
+ *  -# @ref kernels::ClGemmReshapeLhsMatrixKernel (only if the RESHAPED_V1 is selected by the heuristic model)
+ *  -# @ref kernels::ClGemmReshapeRhsMatrixKernel (only if either the RESHAPED_V1 or RESHAPED_ONLY_RHS is selected by the select_gemm_kernel method())
+ *  -# @ref kernels::ClGemmMatrixMultiplyKernel (only if either the NATIVE or RESHAPED_V1 is selected by the select_gemm_kernel method())
+ *  -# @ref kernels::ClGemmMatrixMultiplyReshapedKernel (only if RESHAPED_V1 is selected by the select_gemm_kernel method())
+ *  -# @ref kernels::ClGemmMatrixMultiplyReshapedOnlyRhsKernel (only if RESHAPED_ONLY_RHS is selected by the select_gemm_kernel method())
+ */
+class ClGemm : public IClOperator
+{
+public:
+    /** Constructor */
+    ClGemm();
+    /** Initialise the kernel's inputs and output
+     *
+     * Valid data layouts:
+     * - All
+     *
+     * Valid data type configurations:
+     * |src0         |src1        |src2      |dst            |
+     * |:------------|:-----------|:---------|:--------------|
+     * |F32          |F32         |F32       |F32            |
+     * |F16          |F16         |F16       |F16            |
+     *
+     * @note GEMM: General Matrix Multiply - [alpha * A * B + beta * C].
+     *
+     * @note All tensors must have the same data type.
+     *
+     * @note Whilst the first input tensor can be a vector, the second input tensor must be at least a matrix
+     *
+     * @param[in]  compile_context The compile context to be used.
+     * @param[in]  a               First input tensor  (Matrix or Vector A). Data types supported: F16/F32
+     * @param[in]  b               Second input tensor (Matrix B). Data type supported: same as @p a.
+     * @param[in]  c               Third input tensor  (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a.
+     * @param[out] output          Output tensor. Data type supported: same as @p a
+     * @param[in]  alpha           Weight of the matrix product
+     * @param[in]  beta            Weight of matrix C
+     * @param[in]  gemm_info       (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
+     *                       if the reshape of matrix B should happen only for the first run. GEMMInfo also contains information about the reshaping
+     *                       in case matrix A and matrix B have been already transformed.
+     */
+    void configure(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+    /** Static function to check if given info will lead to a valid configuration
+     *
+     * Similar to ClGemm::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+
+    // Inherited methods overridden:
+    void run(ITensorPack &tensors) override;
+    void prepare(ITensorPack &constants) override;
+    experimental::MemoryRequirements workspace() const override;
+
+private:
+    void configure_native_v1(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+    void configure_reshaped_v1(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+    void configure_reshaped_v2(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+    void configure_reshaped_only_rhs(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+
+    static Status validate_native_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+    static Status validate_reshaped_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+    static Status validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+    static Status validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+
+private:
+    enum AuxTensorIdx
+    {
+        LhsReshape = 0,
+        RhsReshape,
+        Count
+    };
+
+private:
+    std::unique_ptr<kernels::ClGemmMatrixMultiplyKernel>                _mm_kernel;
+    std::unique_ptr<kernels::ClGemmReshapeLhsMatrixKernel>              _reshape_lhs_kernel;
+    std::unique_ptr<kernels::ClGemmReshapeRhsMatrixKernel>              _reshape_rhs_kernel;
+    std::unique_ptr<kernels::ClGemmMatrixMultiplyReshapedKernel>        _mm_reshaped_kernel;
+    std::unique_ptr<kernels::ClGemmMatrixMultiplyReshapedOnlyRhsKernel> _mm_reshaped_only_rhs_kernel;
+    std::unique_ptr<kernels::ClGemmMatrixMultiplyReshapedOnlyRhsKernel> _mm_reshaped_only_rhs_fallback_kernel;
+    TensorInfo                                                          _tmp_a;
+    TensorInfo                                                          _tmp_b;
+    bool                                                                _reshape_b_only_on_first_run;
+    CLGEMMKernelType                                                    _gemm_kernel_type;
+
+    experimental::MemoryRequirements _aux_mem{};
+};
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CLGEMM_H */
diff --git a/src/runtime/gpu/cl/utils/ClAuxTensorHandler.h b/src/runtime/gpu/cl/utils/ClAuxTensorHandler.h
new file mode 100644
index 0000000000..ad893acea5
--- /dev/null
+++ b/src/runtime/gpu/cl/utils/ClAuxTensorHandler.h
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_UTILS_CL_AUX_TENSOR_HANDLER_H
+#define ARM_COMPUTE_CL_UTILS_CL_AUX_TENSOR_HANDLER_H
+
+#include "arm_compute/core/ITensorPack.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+
+#include "support/Cast.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+/* Tensor handler to wrap and handle tensor allocations on workspace buffers */
+class CLAuxTensorHandler
+{
+public:
+    CLAuxTensorHandler(int slot_id, TensorInfo &info, ITensorPack &pack, bool pack_inject = false)
+        : _tensor()
+    {
+        _tensor.allocator()->soft_init(info);
+
+        ICLTensor *packed_tensor = utils::cast::polymorphic_downcast<ICLTensor *>(pack.get_tensor(slot_id));
+        if((packed_tensor == nullptr) || (info.total_size() > packed_tensor->info()->total_size()))
+        {
+            _tensor.allocator()->allocate();
+            if(pack_inject)
+            {
+                pack.add_tensor(slot_id, &_tensor);
+            }
+        }
+        else
+        {
+            _tensor.allocator()->import_memory(packed_tensor->cl_buffer());
+        }
+    }
+
+    CLAuxTensorHandler(TensorInfo &info, ICLTensor &tensor)
+        : _tensor()
+    {
+        _tensor.allocator()->soft_init(info);
+        if(info.total_size() <= tensor.info()->total_size())
+        {
+            _tensor.allocator()->import_memory(tensor.cl_buffer());
+        }
+    }
+
+    ICLTensor *get()
+    {
+        return &_tensor;
+    }
+
+    ICLTensor *operator()()
+    {
+        return &_tensor;
+    }
+
+private:
+    CLTensor _tensor{};
+};
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CL_UTILS_CL_AUX_TENSOR_HANDLER_H */
\ No newline at end of file
diff --git a/tests/CL/Helper.h b/tests/CL/Helper.h
index 5153e98add..b99911e1e6 100644
--- a/tests/CL/Helper.h
+++ b/tests/CL/Helper.h
@@ -29,8 +29,11 @@
 #include "arm_compute/runtime/CL/functions/CLFill.h"
 #include "arm_compute/runtime/IFunction.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
+#include "src/runtime/gpu/cl/IClOperator.h"
+#include "src/runtime/gpu/cl/operators/ClFill.h"
 
 #include "src/core/CL/ICLKernel.h"
+#include "support/Cast.h"
 
 #include <memory>
 
@@ -38,6 +41,86 @@ namespace arm_compute
 {
 namespace test
 {
+/** This template synthetizes a simple IOperator which runs the given kernel K */
+template <typename K>
+class CLSynthetizeOperator : public opencl::IClOperator
+{
+public:
+    /** Configure the kernel.
+     *
+     * @param[in] args Configuration arguments.
+     */
+    template <typename... Args>
+    void configure(Args &&... args)
+    {
+        auto k = std::make_unique<K>();
+        k->configure(CLKernelLibrary::get().get_compile_context(), std::forward<Args>(args)...);
+        _kernel = std::move(k);
+    }
+    /** Configure the kernel setting the GPU target as well
+     *
+     * @param[in] gpu_target GPUTarget to set
+     * @param[in] args       Configuration arguments.
+     */
+    template <typename... Args>
+    void configure(GPUTarget gpu_target, Args &&... args)
+    {
+        auto k = std::make_unique<K>();
+        k->set_target(gpu_target);
+        k->configure(CLKernelLibrary::get().get_compile_context(), std::forward<Args>(args)...);
+        _kernel = std::move(k);
+    }
+    /** Validate input arguments
+     *
+     * @param[in] args Configuration arguments.
+     */
+    template <typename... Args>
+    static Status validate(Args &&... args)
+    {
+        return K::validate(std::forward<Args>(args)...);
+    }
+};
+
+/** As above but this also initializes to zero the input tensor */
+template <typename K, int bordersize>
+class CLSynthetizeOperatorInitOutputWithZeroAndWithZeroConstantBorder : public opencl::IClOperator
+{
+public:
+    /** Configure the kernel.
+     *
+     * @param[in] first  First input argument.
+     * @param[in] second Second input argument.
+     * @param[in] args   Rest of the configuration arguments.
+     */
+    template <typename T, typename... Args>
+    void configure(T first, T second, Args &&... args)
+    {
+        auto cctx = CLKernelLibrary::get().get_compile_context();
+        auto k    = std::make_unique<K>();
+        k->set_target(CLScheduler::get().target());
+        k->configure(cctx, first, second, std::forward<Args>(args)...);
+        _kernel = std::move(k);
+        _border_handler.configure(cctx, first, BorderSize(bordersize), BorderMode::CONSTANT, PixelValue());
+        _fill.configure(cctx, second, PixelValue());
+    }
+
+    // Inherited method overridden:
+    void run(ITensorPack &tensors) override final
+    {
+        ARM_COMPUTE_ERROR_ON_MSG(!_kernel, "The CL kernel or function isn't configured");
+
+        ITensorPack fill_pack = { { ACL_SRC, tensors.get_tensor(TensorType::ACL_DST) } };
+        _fill.run(fill_pack);
+        CLScheduler::get().enqueue_op(_border_handler, tensors);
+        CLScheduler::get().enqueue_op(*_kernel, tensors);
+    }
+
+private:
+    opencl::ClFill             _fill{};           /**< Kernel to initialize the tensor */
+    CLFillBorderKernel         _border_handler{}; /**< Kernel to handle  borders */
+    std::unique_ptr<ICLKernel> _kernel{};         /**< Kernel to run */
+};
+
 /** This template synthetizes an ICLSimpleFunction which runs the given kernel K */
 template <typename K>
 class CLSynthetizeFunction : public ICLSimpleFunction
diff --git a/tests/validation/CL/GEMMLowpMatrixMultiplyReshaped.cpp b/tests/validation/CL/GEMMLowpMatrixMultiplyReshaped.cpp
index 4873a291ab..68a7d055ad 100644
--- a/tests/validation/CL/GEMMLowpMatrixMultiplyReshaped.cpp
+++ b/tests/validation/CL/GEMMLowpMatrixMultiplyReshaped.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -24,8 +24,8 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/framework/Asserts.h"
@@ -42,13 +42,13 @@ namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
 
-// Create function for CLGEMMReshapeLHSMatrixKernel
-using CLGEMMReshapeLHSMatrix = CLSynthetizeFunction<CLGEMMReshapeLHSMatrixKernel>;
+// Create function for ClGemmReshapeLhsMatrixKernel
+using CLGEMMReshapeLHSMatrix = CLSynthetizeOperator<opencl::kernels::ClGemmReshapeLhsMatrixKernel>;
 
-// Create function for CLGEMMReshapeRHSMatrixKernel
-using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction<CLGEMMReshapeRHSMatrixKernel>;
+// Create function for ClGemmReshapeRhsMatrixKernel
+using CLGEMMReshapeRHSMatrix = CLSynthetizeOperator<opencl::kernels::ClGemmReshapeRhsMatrixKernel>;
 
-// Create function for CLGEMMMatrixMultiplyReshapedKernel
+// Create function for CLGEMMLowpMatrixMultiplyReshapedKernel
 using CLGEMMLowpMatrixMultiplyReshaped = CLSynthetizeFunction<CLGEMMLowpMatrixMultiplyReshapedKernel>;
 
 // Fixture for CLGEMMLowpMatrixMultiplyReshaped
diff --git a/tests/validation/CL/GEMMLowpMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMLowpMatrixMultiplyReshapedOnlyRHS.cpp
index fa256280ca..43b86b51e8 100644
--- a/tests/validation/CL/GEMMLowpMatrixMultiplyReshapedOnlyRHS.cpp
+++ b/tests/validation/CL/GEMMLowpMatrixMultiplyReshapedOnlyRHS.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -26,7 +26,7 @@
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
 #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
@@ -46,7 +46,7 @@ namespace validation
 using namespace arm_compute::misc::shape_calculator;
 
 // Create function for CLGEMMReshapeRHSMatrixKernel
-using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction<CLGEMMReshapeRHSMatrixKernel>;
+using CLGEMMReshapeRHSMatrix = CLSynthetizeOperator<opencl::kernels::ClGemmReshapeRhsMatrixKernel>;
 
 // Create function for CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel
 using CLGEMMLowpMatrixMultiplyReshapedOnlyRHS = CLSynthetizeFunction<CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel>;
diff --git a/tests/validation/CL/GEMMMatrixMultiply.cpp b/tests/validation/CL/GEMMMatrixMultiply.cpp
index fdf7f503ec..21e085087d 100644
--- a/tests/validation/CL/GEMMMatrixMultiply.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiply.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -26,7 +26,7 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
@@ -44,9 +44,10 @@ namespace test
 namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::opencl::kernels;
 
 // Create function for CLGEMMMatrixMultiplyKernel
-using CLGEMMMatrixMultiplyNative = CLSynthetizeFunction<CLGEMMMatrixMultiplyKernel>;
+using CLGEMMMatrixMultiplyNative = CLSynthetizeOperator<ClGemmMatrixMultiplyKernel>;
 
 // Fixture for GEMMMatrixMultiplyValidationFixture
 template <typename T>
@@ -140,7 +141,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const bool is_interleaved_transposed = false;
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(12, 14, 13, 1, 1, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -154,7 +155,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const bool is_interleaved_transposed = false;
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(12, 14, 13, 1, 1, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -169,7 +170,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const GEMMReshapeInfo reshape_info  = GEMMReshapeInfo(12, 14, 13, 1, 1, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
         const bool fp_mixed_precision        = true;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -183,7 +184,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const bool is_interleaved_transposed = false;
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(12, 14, 13, 1, 1, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -197,7 +198,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const bool is_interleaved_transposed = false;
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(12, 14, 13, 1, 1, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -214,7 +215,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(12, 14, 13, 1, 1, 0, false, true);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
         const bool fp_mixed_precision        = false;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -231,7 +232,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(12, 14, 13, 1, 1, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
         const bool fp_mixed_precision        = false;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -246,7 +247,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const bool is_interleaved_transposed = false;
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(12, 14, 13, 1, 1, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, nullptr, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 }
diff --git a/tests/validation/CL/GEMMMatrixMultiplyInterleavedTransposed.cpp b/tests/validation/CL/GEMMMatrixMultiplyInterleavedTransposed.cpp
index d6507a06c4..e47518ad7d 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyInterleavedTransposed.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyInterleavedTransposed.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -26,9 +26,9 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
@@ -46,15 +46,16 @@ namespace test
 namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::opencl::kernels;
 
-// Create function for CLGEMMReshapeLHSMatrixKernel
-using CLGEMMReshapeLHSMatrix = CLSynthetizeFunction<CLGEMMReshapeLHSMatrixKernel>;
+// Create function for ClGemmReshapeLhsMatrixKernel
+using CLGEMMReshapeLHSMatrix = CLSynthetizeOperator<ClGemmReshapeLhsMatrixKernel>;
 
-// Create function for CLGEMMReshapeRHSMatrixKernel
-using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction<CLGEMMReshapeRHSMatrixKernel>;
+// Create function for ClGemmReshapeRhsMatrixKernel
+using CLGEMMReshapeRHSMatrix = CLSynthetizeOperator<ClGemmReshapeRhsMatrixKernel>;
 
-// Create function for CLGEMMMatrixMultiplyKernel
-using CLGEMMMatrixMultiplyReshaped = CLSynthetizeFunction<CLGEMMMatrixMultiplyKernel>;
+// Create function for ClGemmMatrixMultiplyKernel
+using CLGEMMMatrixMultiplyReshaped = CLSynthetizeOperator<ClGemmMatrixMultiplyKernel>;
 
 // Fixture for GEMMMatrixMultiplyInterleavedTransposedValidationFixture
 template <typename T>
@@ -166,7 +167,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
         const bool fp_mixed_precision        = false;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -183,7 +184,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
         const bool fp_mixed_precision        = false;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -200,7 +201,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, true);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
         const bool fp_mixed_precision        = false;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -217,7 +218,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
         const bool fp_mixed_precision        = false;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 
@@ -234,7 +235,7 @@ TEST_CASE(Negative, framework::DatasetMode::ALL)
         const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(16, 24, 13, 2, 4, 0, false, false);
         const GPUTarget gpu_target           = GPUTarget::MIDGARD;
         const bool fp_mixed_precision        = false;
-        const auto status    = CLGEMMMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
+        const auto status    = ClGemmMatrixMultiplyKernel::validate(&lhs, &rhs, &bias, &out, alpha, beta, is_interleaved_transposed, reshape_info, gpu_target, fp_mixed_precision);
         ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
     }
 }
diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
index ec6b87fbae..a737c687c4 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -26,7 +26,7 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
@@ -44,9 +44,10 @@ namespace test
 namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::opencl::kernels;
 
-// Create function for CLGEMMMatrixMultiplyNativeKernel
-using CLGEMMMatrixMultiplyNative = CLSynthetizeFunction<CLGEMMMatrixMultiplyNativeKernel>;
+// Create function for ClGemmMatrixMultiplyNativeKernel
+using CLGEMMMatrixMultiplyNative = CLSynthetizeOperator<ClGemmMatrixMultiplyNativeKernel>;
 
 // Fixture for CLGEMMMatrixMultiplyNative
 template <typename T>
@@ -184,7 +185,7 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned
 
     // Create and configure function
     CLGEMMMatrixMultiplyNative gemm;
-    gemm.configure(&lhs, &rhs, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, kernel_info);
+    gemm.configure(lhs.info(), rhs.info(), bias.info(), dst.info(), 1.0f, 1.0f, lhs_info, rhs_info, kernel_info);
 }
 } // namespace
 
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
index 52afb716e4..6f368a9650 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2020 Arm Limited.
+ * Copyright (c) 2018-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -26,9 +26,9 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
@@ -46,15 +46,16 @@ namespace test
 namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::opencl::kernels;
 
-// Create function for CLGEMMReshapeLHSMatrixKernel
-using CLGEMMReshapeLHSMatrix = CLSynthetizeFunction<CLGEMMReshapeLHSMatrixKernel>;
+// Create function for ClGemmReshapeLhsMatrixKernel
+using CLGEMMReshapeLHSMatrix = CLSynthetizeOperator<ClGemmReshapeLhsMatrixKernel>;
 
-// Create function for CLGEMMReshapeRHSMatrixKernel
-using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction<CLGEMMReshapeRHSMatrixKernel>;
+// Create function for ClGemmReshapeRhsMatrixKernel
+using CLGEMMReshapeRHSMatrix = CLSynthetizeOperator<ClGemmReshapeRhsMatrixKernel>;
 
-// Create function for CLGEMMMatrixMultiplyReshapedKernel
-using CLGEMMMatrixMultiplyReshaped = CLSynthetizeFunction<CLGEMMMatrixMultiplyReshapedKernel>;
+// Create function for ClGemmMatrixMultiplyReshapedKernel
+using CLGEMMMatrixMultiplyReshaped = CLSynthetizeOperator<ClGemmMatrixMultiplyReshapedKernel>;
 
 // Fixture for CLGEMMMatrixMultiplyReshaped
 template <typename T>
@@ -327,7 +328,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi
                framework::dataset::make("Expected", { true, true, false, false, false, true, true,true})),
                     input0_info ,input1_info, input2_info, output_info, lhs_info, rhs_info, gemm_info, expected)
 {
-   ARM_COMPUTE_EXPECT(bool(CLGEMMMatrixMultiplyReshapedKernel::validate(&input0_info.clone()->set_is_resizable(true),
+   ARM_COMPUTE_EXPECT(bool(ClGemmMatrixMultiplyReshapedKernel::validate(&input0_info.clone()->set_is_resizable(true),
                                                           &input1_info.clone()->set_is_resizable(true),
                                                           &input2_info.clone()->set_is_resizable(true),
                                                           &output_info.clone()->set_is_resizable(true),1.f,1.f,
@@ -562,7 +563,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi
                                                       false})),
                     input0_info ,input1_info, input2_info, output_info, lhs_info, rhs_info, gemm_info, expected)
 {
-   ARM_COMPUTE_EXPECT(bool(CLGEMMMatrixMultiplyReshapedKernel::validate(&input0_info.clone()->set_is_resizable(true),
+   ARM_COMPUTE_EXPECT(bool(ClGemmMatrixMultiplyReshapedKernel::validate(&input0_info.clone()->set_is_resizable(true),
                                                           &input1_info.clone()->set_is_resizable(true),
                                                           &input2_info.clone()->set_is_resizable(true),
                                                           &output_info.clone()->set_is_resizable(true),1.f,1.f,
@@ -933,7 +934,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi
                                                       false})),
                     input0_info ,input1_info, input2_info, output_info, lhs_info, rhs_info, gemm_info, expected)
 {
-   ARM_COMPUTE_EXPECT(bool(CLGEMMMatrixMultiplyReshapedKernel::validate(&input0_info.clone()->set_is_resizable(true),
+   ARM_COMPUTE_EXPECT(bool(ClGemmMatrixMultiplyReshapedKernel::validate(&input0_info.clone()->set_is_resizable(true),
                                                           &input1_info.clone()->set_is_resizable(true),
                                                           &input2_info.clone()->set_is_resizable(true),
                                                           &output_info.clone()->set_is_resizable(true),1.f,1.f,
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
index ebcecb8b78..88e99bcfef 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
@@ -26,8 +26,8 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
@@ -45,12 +45,13 @@ namespace test
 namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::opencl::kernels;
 
-// Create function for CLGEMMReshapeRHSMatrixKernel
-using CLGEMMReshapeRHSMatrix = CLSynthetizeFunction<CLGEMMReshapeRHSMatrixKernel>;
+// Create function for ClGemmReshapeRhsMatrixKernel
+using CLGEMMReshapeRHSMatrix = CLSynthetizeOperator<ClGemmReshapeRhsMatrixKernel>;
 
-// Create function for CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
-using CLGEMMMatrixMultiplyReshapedOnlyRHS = CLSynthetizeFunction<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>;
+// Create function for ClGemmMatrixMultiplyReshapedOnlyRhsKernel
+using CLGEMMMatrixMultiplyReshapedOnlyRHS = CLSynthetizeOperator<ClGemmMatrixMultiplyReshapedOnlyRhsKernel>;
 
 // Fixture for CLGEMMMatrixMultiplyReshapedOnlyRHS
 template <typename T>
diff --git a/tests/validation/CL/GEMMReshapeLHSMatrix.cpp b/tests/validation/CL/GEMMReshapeLHSMatrix.cpp
index 34c37dffde..f995608308 100644
--- a/tests/validation/CL/GEMMReshapeLHSMatrix.cpp
+++ b/tests/validation/CL/GEMMReshapeLHSMatrix.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2020 Arm Limited.
+ * Copyright (c) 2018-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -25,7 +25,7 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
@@ -43,9 +43,10 @@ namespace test
 namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::opencl::kernels;
 
 // Initialize the output tensor with zero and fill the border with zero
-using CLGEMMReshapeLHSMatrix = CLSynthetizeFunctionInitOutputWithZeroAndWithZeroConstantBorder<CLGEMMReshapeLHSMatrixKernel, 16>;
+using CLGEMMReshapeLHSMatrix = CLSynthetizeOperatorInitOutputWithZeroAndWithZeroConstantBorder<ClGemmReshapeLhsMatrixKernel, 16>;
 
 template <typename T>
 using CLGEMMReshapeLHSMatrixFixture = GEMMReshapeLHSMatrixValidationFixture<CLTensor, CLAccessor, CLGEMMReshapeLHSMatrix, T, false>;
diff --git a/tests/validation/CL/GEMMReshapeRHSMatrix.cpp b/tests/validation/CL/GEMMReshapeRHSMatrix.cpp
index 14048e81ec..ff1240ea2e 100644
--- a/tests/validation/CL/GEMMReshapeRHSMatrix.cpp
+++ b/tests/validation/CL/GEMMReshapeRHSMatrix.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2020 Arm Limited.
+ * Copyright (c) 2018-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -25,7 +25,7 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
@@ -73,9 +73,10 @@ const auto i_values = framework::dataset::make("interleave", { true, false });
 } // namespace
 
 using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::opencl::kernels;
 
 // Initialize the output tensor with zero and fill the border with zero
-using CLGEMMReshapeRHSMatrix = CLSynthetizeFunctionInitOutputWithZeroAndWithZeroConstantBorder<CLGEMMReshapeRHSMatrixKernel, 16>;
+using CLGEMMReshapeRHSMatrix = CLSynthetizeOperatorInitOutputWithZeroAndWithZeroConstantBorder<ClGemmReshapeRhsMatrixKernel, 16>;
 
 template <typename T>
 using CLGEMMReshapeRHSMatrixFixture = GEMMReshapeRHSMatrixValidationFixture<CLTensor, CLAccessor, CLGEMMReshapeRHSMatrix, T>;
@@ -117,7 +118,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
     rhs_info.transpose = true;
     rhs_info.interleave = true;
 
-    bool has_error = bool(CLGEMMReshapeRHSMatrixKernel::validate(&input_info.clone()->set_is_resizable(false), (output_info.total_size() == 0) ? nullptr : &output_info.clone()->set_is_resizable(false), rhs_info));
+    bool has_error = bool(ClGemmReshapeRhsMatrixKernel::validate(&input_info.clone()->set_is_resizable(false), (output_info.total_size() == 0) ? nullptr : &output_info.clone()->set_is_resizable(false), rhs_info));
     ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS);
 }
 
@@ -158,9 +159,9 @@ DATA_TEST_CASE(ValidatePadding, framework::DatasetMode::ALL, combine(combine(com
         padding = round_up_width - output_shape[0];
     }
 
-    CLGEMMReshapeRHSMatrixKernel kernel;
+    ClGemmReshapeRhsMatrixKernel kernel;
 
-    kernel.configure(&input, &output, rhs_info);
+    kernel.configure(CLKernelLibrary::get().get_compile_context(), input.info(), output.info(), rhs_info);
 
     ARM_COMPUTE_EXPECT((output.info()->padding().right == padding), framework::LogLevel::ERRORS);
 }
diff --git a/tests/validation/CL/UNIT/DynamicTensor.cpp b/tests/validation/CL/UNIT/DynamicTensor.cpp
index 833256039e..ad2d4892ba 100644
--- a/tests/validation/CL/UNIT/DynamicTensor.cpp
+++ b/tests/validation/CL/UNIT/DynamicTensor.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2020 Arm Limited.
+ * Copyright (c) 2019-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -29,7 +29,6 @@
 #include "arm_compute/runtime/MemoryManagerOnDemand.h"
 #include "arm_compute/runtime/PoolManager.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "src/core/CL/kernels/CLIm2ColKernel.h"
 #include "src/core/CL/kernels/CLL2NormalizeLayerKernel.h"
 #include "src/core/CL/kernels/CLReductionOperationKernel.h"
diff --git a/tests/validation/CL/UNIT/WeightsRetention.cpp b/tests/validation/CL/UNIT/WeightsRetention.cpp
index acf795e48b..1965f0f7a5 100644
--- a/tests/validation/CL/UNIT/WeightsRetention.cpp
+++ b/tests/validation/CL/UNIT/WeightsRetention.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2020 Arm Limited.
+ * Copyright (c) 2018-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -24,16 +24,6 @@
 #include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
 #include "src/core/CL/kernels/CLDepthConvertLayerKernel.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
-#include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
 #include "tests/AssetsLibrary.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/Globals.h"
diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h
index 9ad27c782e..c118da66ae 100644
--- a/tests/validation/fixtures/GEMMFixture.h
+++ b/tests/validation/fixtures/GEMMFixture.h
@@ -175,7 +175,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename T, typename GEMMOperatorType>
 class GEMMMatrixMultiplyValidationFixture : public framework::Fixture
 {
 public:
@@ -226,8 +226,8 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        GEMMFunctionType gemm;
-        gemm.configure(gpu_arch, &lhs, &rhs, &bias, &dst, alpha, beta, false, reshape_info, fp16_mixed_precision, act_info);
+        GEMMOperatorType gemm;
+        gemm.configure(gpu_arch, lhs.info(), rhs.info(), bias.info(), dst.info(), alpha, beta, false, reshape_info, fp16_mixed_precision, act_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -252,7 +252,12 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        gemm.run();
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -294,7 +299,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename T, typename GEMMOperatorType>
 class GEMMMatrixMultiply3DValidationFixture : public framework::Fixture
 {
 public:
@@ -344,8 +349,8 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        GEMMFunctionType gemm;
-        gemm.configure(gpu_arch, &lhs, &rhs, &bias, &dst, alpha, beta, false, reshape_info, fp16_mixed_precision, act_info);
+        GEMMOperatorType gemm;
+        gemm.configure(gpu_arch, lhs.info(), rhs.info(), bias.info(), dst.info(), alpha, beta, false, reshape_info, fp16_mixed_precision, act_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -370,7 +375,12 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        gemm.run();
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -411,7 +421,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMOperatorType>
 class GEMMMatrixMultiplyInterleavedTransposedValidationFixture : public framework::Fixture
 {
 public:
@@ -478,12 +488,12 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeLHSFunctionType reshape_lhs;
-        ReshapeRHSFunctionType reshape_rhs;
-        GEMMFunctionType       gemm;
-        reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(gpu_arch, &lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, true, reshape_info, fp16_mixed_precision, act_info);
+        ReshapeLHSOperatorType reshape_lhs;
+        ReshapeRHSOperatorType reshape_rhs;
+        GEMMOperatorType       gemm;
+        reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
+        gemm.configure(gpu_arch, lhs_reshaped.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, true, reshape_info, fp16_mixed_precision, act_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -516,9 +526,16 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        reshape_lhs.run();
-        reshape_rhs.run();
-        gemm.run();
+        ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
+        reshape_lhs.run(reshape_lhs_pack);
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -560,7 +577,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMOperatorType>
 class GEMMMatrixMultiplyInterleavedTransposed3DValidationFixture : public framework::Fixture
 {
 public:
@@ -626,12 +643,12 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeLHSFunctionType reshape_lhs;
-        ReshapeRHSFunctionType reshape_rhs;
-        GEMMFunctionType       gemm;
-        reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(gpu_arch, &lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, true, reshape_info, fp16_mixed_precision, act_info);
+        ReshapeLHSOperatorType reshape_lhs;
+        ReshapeRHSOperatorType reshape_rhs;
+        GEMMOperatorType       gemm;
+        reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
+        gemm.configure(gpu_arch, lhs_reshaped.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, true, reshape_info, fp16_mixed_precision, act_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -664,9 +681,16 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        reshape_lhs.run();
-        reshape_rhs.run();
-        gemm.run();
+        ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
+        reshape_lhs.run(reshape_lhs_pack);
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -707,7 +731,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType, bool fp_mixed_precision = false>
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMOperatorType, bool fp_mixed_precision = false>
 class GEMMMatrixMultiplyReshapedValidationFixture : public framework::Fixture
 {
 public:
@@ -786,9 +810,9 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeLHSFunctionType reshape_lhs;
-        ReshapeRHSFunctionType reshape_rhs;
-        GEMMFunctionType       gemm;
+        ReshapeLHSOperatorType reshape_lhs;
+        ReshapeRHSOperatorType reshape_rhs;
+        GEMMOperatorType       gemm;
 
         validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
         validate_result = validate_result || !rhs_info.export_to_cl_image;
@@ -797,9 +821,9 @@ protected:
             return nullptr;
         }
 
-        reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
+        gemm.configure(lhs_reshaped.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -832,9 +856,16 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        reshape_lhs.run();
-        reshape_rhs.run();
-        gemm.run();
+        ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
+        reshape_lhs.run(reshape_lhs_pack);
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -884,7 +915,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType, bool fp_mixed_precision = false>
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMOperatorType, bool fp_mixed_precision = false>
 class GEMMMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture
 {
 public:
@@ -960,9 +991,9 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeLHSFunctionType reshape_lhs;
-        ReshapeRHSFunctionType reshape_rhs;
-        GEMMFunctionType       gemm;
+        ReshapeLHSOperatorType reshape_lhs;
+        ReshapeRHSOperatorType reshape_rhs;
+        GEMMOperatorType       gemm;
 
         validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
         validate_result = validate_result || !rhs_info.export_to_cl_image;
@@ -971,9 +1002,9 @@ protected:
             return nullptr;
         }
 
-        reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
+        gemm.configure(lhs_reshaped.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -1006,9 +1037,16 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        reshape_lhs.run();
-        reshape_rhs.run();
-        gemm.run();
+        ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
+        reshape_lhs.run(reshape_lhs_pack);
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -1057,7 +1095,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSOperatorType, typename GEMMOperatorType>
 class GEMMMatrixMultiplyReshapedOnlyRHSValidationFixture : public framework::Fixture
 {
 public:
@@ -1131,8 +1169,8 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeRHSFunctionType reshape_rhs;
-        GEMMFunctionType       gemm;
+        ReshapeRHSOperatorType reshape_rhs;
+        GEMMOperatorType       gemm;
 
         validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
         validate_result = validate_result || !rhs_info.export_to_cl_image;
@@ -1141,8 +1179,8 @@ protected:
             return nullptr;
         }
 
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
+        gemm.configure(lhs.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -1173,8 +1211,14 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        reshape_rhs.run();
-        gemm.run();
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -1217,7 +1261,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSOperatorType, typename GEMMOperatorType>
 class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture
 {
 public:
@@ -1289,8 +1333,8 @@ protected:
 
         // The output tensor will be auto-initialized within the function
         // Create and configure function
-        ReshapeRHSFunctionType reshape_rhs;
-        GEMMFunctionType       gemm;
+        ReshapeRHSOperatorType reshape_rhs;
+        GEMMOperatorType       gemm;
 
         validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
         validate_result = validate_result || !rhs_info.export_to_cl_image;
@@ -1299,8 +1343,8 @@ protected:
             return nullptr;
         }
 
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
+        gemm.configure(lhs.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         if(has_pad_y)
         {
@@ -1338,8 +1382,14 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        reshape_rhs.run();
-        gemm.run();
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -1381,7 +1431,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename T, typename GEMMOperatorType>
 class GEMMMatrixMultiplyNativeValidationFixture : public framework::Fixture
 {
 public:
@@ -1445,8 +1495,8 @@ protected:
         kernel_info.activation_info         = act_info;
 
         // Create and configure function
-        GEMMFunctionType gemm;
-        gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        GEMMOperatorType gemm;
+        gemm.configure(lhs.info(), rhs.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -1471,7 +1521,12 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        gemm.run();
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
@@ -1513,7 +1568,7 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename T, typename GEMMOperatorType>
 class GEMMMatrixMultiplyNative3DValidationFixture : public framework::Fixture
 {
 public:
@@ -1576,8 +1631,8 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        GEMMFunctionType gemm;
-        gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info);
+        GEMMOperatorType gemm;
+        gemm.configure(lhs.info(), rhs.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
@@ -1602,7 +1657,12 @@ protected:
         fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        gemm.run();
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        gemm.run(gemm_pack);
 
         return dst;
     }
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index d7fe96cd3d..5cf210bab4 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -869,7 +869,7 @@ protected:
     SimpleTensor<int16_t> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
 class GEMMLowpMatrixMultiplyReshapedValidationFixture : public framework::Fixture
 {
 public:
@@ -939,11 +939,11 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeLHSFunctionType reshape_lhs;
-        ReshapeRHSFunctionType reshape_rhs;
+        ReshapeLHSOperatorType reshape_lhs;
+        ReshapeRHSOperatorType reshape_rhs;
         GEMMFunctionType       gemm;
-        reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
+        reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
         gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
@@ -969,8 +969,10 @@ protected:
         fill(AccessorType(rhs), 1);
 
         // Compute GEMM
-        reshape_lhs.run();
-        reshape_rhs.run();
+        ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
+        reshape_lhs.run(reshape_lhs_pack);
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
         gemm.run();
 
         return dst;
@@ -1017,7 +1019,7 @@ protected:
     SimpleTensor<int32_t> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
 class GEMMLowpMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture
 {
 public:
@@ -1091,11 +1093,11 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeLHSFunctionType reshape_lhs;
-        ReshapeRHSFunctionType reshape_rhs;
+        ReshapeLHSOperatorType reshape_lhs;
+        ReshapeRHSOperatorType reshape_rhs;
         GEMMFunctionType       gemm;
-        reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
+        reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
         gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h));
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
@@ -1121,8 +1123,10 @@ protected:
         fill(AccessorType(rhs), 1);
 
         // Compute GEMM
-        reshape_lhs.run();
-        reshape_rhs.run();
+        ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
+        reshape_lhs.run(reshape_lhs_pack);
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
         gemm.run();
 
         return dst;
@@ -1171,7 +1175,7 @@ protected:
     SimpleTensor<int32_t> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
 class GEMMLowpMatrixMultiplyReshapedOnlyRHSValidationFixture : public framework::Fixture
 {
 public:
@@ -1244,9 +1248,9 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeRHSFunctionType reshape_rhs;
+        ReshapeRHSOperatorType reshape_rhs;
         GEMMFunctionType       gemm;
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
         gemm.configure(&lhs, &rhs_reshaped, &dst, gemm_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
@@ -1270,7 +1274,8 @@ protected:
         fill(AccessorType(rhs), 1);
 
         // Compute GEMM
-        reshape_rhs.run();
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
         gemm.run();
 
         return dst;
@@ -1312,7 +1317,7 @@ protected:
     SimpleTensor<int32_t> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+template <typename TensorType, typename AccessorType, typename ReshapeRHSOperatorType, typename GEMMFunctionType>
 class GEMMLowpMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture
 {
 public:
@@ -1389,9 +1394,9 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        ReshapeRHSFunctionType reshape_rhs;
+        ReshapeRHSOperatorType reshape_rhs;
         GEMMFunctionType       gemm;
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
         gemm.configure(&lhs, &rhs_reshaped, &dst, gemm_info);
 
         ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
@@ -1415,7 +1420,8 @@ protected:
         fill(AccessorType(rhs), 1);
 
         // Compute GEMM
-        reshape_rhs.run();
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
         gemm.run();
 
         return dst;
diff --git a/tests/validation/fixtures/GEMMReshapeLHSMatrixFixture.h b/tests/validation/fixtures/GEMMReshapeLHSMatrixFixture.h
index 70bafcc143..a9d6c9b6aa 100644
--- a/tests/validation/fixtures/GEMMReshapeLHSMatrixFixture.h
+++ b/tests/validation/fixtures/GEMMReshapeLHSMatrixFixture.h
@@ -46,7 +46,7 @@ namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
 
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool reinterpret_input_as_3d = false>
+template <typename TensorType, typename AccessorType, typename OperatorType, typename T, bool reinterpret_input_as_3d = false>
 class GEMMReshapeLHSMatrixValidationFixture : public framework::Fixture
 {
 public:
@@ -86,8 +86,8 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        FunctionType gemm_lhs_reshape;
-        gemm_lhs_reshape.configure(&src, &dst, lhs_info, reinterpret_input_as_3d);
+        OperatorType gemm_lhs_reshape;
+        gemm_lhs_reshape.configure(src.info(), dst.info(), lhs_info, reinterpret_input_as_3d);
 
         ARM_COMPUTE_ASSERT(src.info()->is_resizable());
 
@@ -104,7 +104,8 @@ protected:
         fill(AccessorType(src));
 
         // Compute GEMM LHS matrix reshape function
-        gemm_lhs_reshape.run();
+        ITensorPack tensors = { { ACL_SRC, &src }, { ACL_DST, &dst } };
+        gemm_lhs_reshape.run(tensors);
 
         return dst;
     }
diff --git a/tests/validation/fixtures/GEMMReshapeRHSMatrixFixture.h b/tests/validation/fixtures/GEMMReshapeRHSMatrixFixture.h
index 1428adb3a7..cdb3ec3944 100644
--- a/tests/validation/fixtures/GEMMReshapeRHSMatrixFixture.h
+++ b/tests/validation/fixtures/GEMMReshapeRHSMatrixFixture.h
@@ -46,7 +46,7 @@ namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;
 
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+template <typename TensorType, typename AccessorType, typename OperatorType, typename T>
 class GEMMReshapeRHSMatrixValidationFixture : public framework::Fixture
 {
 public:
@@ -85,8 +85,8 @@ protected:
         // The output tensor will be auto-initialized within the function
 
         // Create and configure function
-        FunctionType gemm_rhs_reshape;
-        gemm_rhs_reshape.configure(&src, &dst, rhs_info);
+        OperatorType gemm_rhs_reshape;
+        gemm_rhs_reshape.configure(src.info(), dst.info(), rhs_info);
 
         ARM_COMPUTE_ASSERT(src.info()->is_resizable());
 
@@ -103,7 +103,8 @@ protected:
         fill(AccessorType(src));
 
         // Compute GEMM RHS matrix reshape function
-        gemm_rhs_reshape.run();
+        ITensorPack tensors = { { ACL_SRC, &src }, { ACL_DST, &dst } };
+        gemm_rhs_reshape.run(tensors);
 
         return dst;
     }